Animated visualizations of network activity across network address spaces

ABSTRACT

Techniques and mechanisms are disclosed for generating visualizations which graphically depict network activity occurring between pairs of networked computing devices. The visualizations are based on data indicating the network activity, where the network activity can involve devices having any network addresses within an entire network address space (e.g., any address within the Internet Protocol version v4 (IPv4) or IPv6 network address space), or within some subset of an entire network address space. The ability to visualize high-level information related to network activity occurring across an entire network address space enables network analysts and other users to readily analyze characteristics of computer networks which otherwise might not be evident or difficult to obtain using other types of visualizations.

CROSS-REFERENCE TO RELATED APPLICATION

This application claims benefit under 35 U.S.C. § 120 as a continuationof U.S. application Ser. No. 16/779,056, filed Jan. 31, 2020, which is acontinuation of U.S. application Ser. No. 15/421,389, filed Jan. 31,2017, now U.S. Pat. No. 10,594,576, the entire contents of which arehereby incorporated by reference as if fully set forth herein. Theapplicant(s) hereby rescind any disclaimer of claim scope in the parentapplication(s) or the prosecution history thereof and advise the USPTOthat the claims in this application may be broader than any claim in theparent application(s).

BACKGROUND

Network analysts and other users often have reason to examine networkactivity associated with individual computing devices or with arelatively small number of networked computing devices connected to alarger network. This information can be useful, for example, to diagnosenetwork performance issues, to investigate potential network securityissues, and for other purposes. For example, a network analyst might usea security information and event management (SIEM) application tocapture network traffic information and to view information aboutsecurity alerts and other information related to individual devices on anetwork.

While information about the activity of one or a small number ofnetworked computing devices can be useful, the ability to obtain a“10,000-foot view” of network activity involving a large number ofnetworked devices can be also useful in many situations. For example, anetwork analyst examining a corporate network for the first time mightdesire high-level information related to the activity of devices spreadacross the entire corporate network. This high-level information ofinterest might include, for example, information related to otherinternal and external devices with which the devices of the corporatenetwork are communicating.

BRIEF DESCRIPTION OF THE DRAWINGS

In the drawings:

FIG. 1 illustrates a networked computer environment in which anembodiment may be implemented;

FIG. 2 illustrates a block diagram of an example data intake and querysystem in which an embodiment may be implemented;

FIG. 3 is a flow diagram that illustrates how indexers process, index,and store data received from forwarders in accordance with the disclosedembodiments;

FIG. 4 is a flow diagram that illustrates how a search head and indexersperform a search query in accordance with the disclosed embodiments;

FIG. 5 illustrates a scenario where a common customer ID is found amonglog data received from three disparate sources in accordance with thedisclosed embodiments;

FIG. 6A illustrates a search screen in accordance with the disclosedembodiments;

FIG. 6B illustrates a data summary dialog that enables a user to selectvarious data sources in accordance with the disclosed embodiments;

FIGS. 7A-7D illustrate a series of user interface screens for an exampledata model-driven report generation interface in accordance with thedisclosed embodiments;

FIG. 8 illustrates an example search query received from a client andexecuted by search peers in accordance with the disclosed embodiments;

FIG. 9A illustrates a key indicators view in accordance with thedisclosed embodiments;

FIG. 9B illustrates an incident review dashboard in accordance with thedisclosed embodiments;

FIG. 9C illustrates a proactive monitoring tree in accordance with thedisclosed embodiments;

FIG. 9D illustrates a user interface screen displaying both log data andperformance data in accordance with the disclosed embodiments;

FIG. 10 illustrates a block diagram of an example cloud-based dataintake and query system in which an embodiment may be implemented;

FIG. 11 illustrates a block diagram of an example data intake and querysystem that performs searches across external data systems in accordancewith the disclosed embodiments;

FIGS. 12-14 illustrate a series of user interface screens for an exampledata model-driven report generation interface in accordance with thedisclosed embodiments;

FIGS. 15-17 illustrate example visualizations generated by a reportingapplication in accordance with the disclosed embodiments;

FIG. 18 illustrates an example user interface screen displaying anetwork activity visualization in accordance with the disclosedembodiments;

FIG. 19 is a flow diagram that illustrates a method for generating andcausing display of a network activity visualization in accordance withthe disclosed embodiments;

FIG. 20 illustrates an interface element displaying detailed informationrelated to a selected data point of a network activity visualization inaccordance with the disclosed embodiments;

FIG. 21 illustrates a user interface screen including example interfaceelements which enable various types of interaction with a networkactivity visualization in accordance with the disclosed embodiments; and

FIG. 22 illustrates a computer system upon which an embodiment may beimplemented.

DETAILED DESCRIPTION

Embodiments are described herein according to the following outline:

-   -   1.0. General Overview    -   2.0. Operating Environment        -   2.1. Host Devices        -   2.2. Client Devices        -   2.3. Client Device Applications        -   2.4. Data Server System        -   2.5. Data Ingestion            -   2.5.1. Input            -   2.5.2. Parsing            -   2.5.3. Indexing        -   2.6. Query Processing        -   2.7. Field Extraction        -   2.8. Example Search Screen        -   2.9. Data Modelling        -   2.10. Acceleration Techniques            -   2.10.1. Aggregation Technique            -   2.10.2. Keyword Index            -   2.10.3. High Performance Analytics Store            -   2.10.4. Accelerating Report Generation        -   2.11. Security Features        -   2.12. Data Center Monitoring        -   2.13. Cloud-Based System Overview        -   2.14. Searching Externally Archived Data            -   2.14.1. ERP Process Features    -   3.0. Functional Overview        -   3.1. Network Activity Visualization Overview        -   3.2. Generating Visualizations Based on Network Activity            Data        -   3.3. Interacting with Network Activity Visualizations    -   4.0. Example Embodiments    -   5.0. Implementation Mechanism—Hardware Overview    -   6.0. Extensions and Alternatives

1.0. GENERAL OVERVIEW

Modern data centers and other computing environments can compriseanywhere from a few host computer systems to thousands of systemsconfigured to process data, service requests from remote clients, andperform numerous other computational tasks. During operation, variouscomponents within these computing environments often generatesignificant volumes of machine-generated data. For example, machine datais generated by various components in the information technology (IT)environments, such as servers, sensors, routers, mobile devices,Internet of Things (IoT) devices, etc. Machine-generated data caninclude system logs, network packet data, sensor data, applicationprogram data, error logs, stack traces, system performance data, etc. Ingeneral, machine-generated data can also include performance data,diagnostic information, and many other types of data that can beanalyzed to diagnose performance problems, monitor user interactions,and to derive other insights.

A number of tools are available to analyze machine data, that is,machine-generated data. In order to reduce the size of the potentiallyvast amount of machine data that may be generated, many of these toolstypically pre-process the data based on anticipated data analysis needs.For example, pre-specified data items may be extracted from the machinedata and stored in a database to facilitate efficient retrieval andanalysis of those data items at search time. However, the rest of themachine data typically is not saved and discarded during pre-processing.As storage capacity becomes progressively cheaper and more plentiful,there are fewer incentives to discard these portions of machine data andmany reasons to retain more of the data.

This plentiful storage capacity is presently making it feasible to storemassive quantities of minimally processed machine data for laterretrieval and analysis. In general, storing minimally processed machinedata and performing analysis operations at search time can providegreater flexibility because it enables an analyst to search all of themachine data, instead of searching only a pre-specified set of dataitems. This may enable an analyst to investigate different aspects ofthe machine data that previously were unavailable for analysis.

However, analyzing and searching massive quantities of machine datapresents a number of challenges. For example, a data center, servers, ornetwork appliances may generate many different types and formats ofmachine data (e.g., system logs, network packet data (e.g., wire data,etc.), sensor data, application program data, error logs, stack traces,system performance data, operating system data, virtualization data,etc.) from thousands of different components, which can collectively bevery time-consuming to analyze. In another example, mobile devices maygenerate large amounts of information relating to data accesses,application performance, operating system performance, networkperformance, etc. There can be millions of mobile devices that reportthese types of information.

These challenges can be addressed by using an event-based data intakeand query system, such as the SPLUNK® ENTERPRISE system developed bySplunk Inc. of San Francisco, California. The SPLUNK® ENTERPRISE systemis the leading platform for providing real-time operational intelligencethat enables organizations to collect, index, and searchmachine-generated data from various websites, applications, servers,networks, and mobile devices that power their businesses. The SPLUNK®ENTERPRISE system is particularly useful for analyzing data which iscommonly found in system log files, network data, and other data inputsources. Although many of the techniques described herein are explainedwith reference to a data intake and query system similar to the SPLUNK®ENTERPRISE system, these techniques are also applicable to other typesof data systems.

In the SPLUNK® ENTERPRISE system, machine-generated data are collectedand stored as “events”. An event comprises a portion of themachine-generated data and is associated with a specific point in time.For example, events may be derived from “time series data,” where thetime series data comprises a sequence of data points (e.g., performancemeasurements from a computer system, etc.) that are associated withsuccessive points in time. In general, each event can be associated witha timestamp that is derived from the raw data in the event, determinedthrough interpolation between temporally proximate events having knowntimestamps, or determined based on other configurable rules forassociating timestamps with events, etc.

In some instances, machine data can have a predefined format, where dataitems with specific data formats are stored at predefined locations inthe data. For example, the machine data may include data stored asfields in a database table. In other instances, machine data may nothave a predefined format, that is, the data is not at fixed, predefinedlocations, but the data does have repeatable patterns and is not random.This means that some machine data can comprise various data items ofdifferent data types and that may be stored at different locationswithin the data. For example, when the data source is an operatingsystem log, an event can include one or more lines from the operatingsystem log containing raw data that includes different types ofperformance and diagnostic information associated with a specific pointin time.

Examples of components which may generate machine data from which eventscan be derived include, but are not limited to, web servers, applicationservers, databases, firewalls, routers, operating systems, and softwareapplications that execute on computer systems, mobile devices, sensors,Internet of Things (IoT) devices, etc. The data generated by such datasources can include, for example and without limitation, server logfiles, activity log files, configuration files, messages, network packetdata, performance measurements, sensor measurements, etc.

The SPLUNK® ENTERPRISE system uses flexible schema to specify how toextract information from the event data. A flexible schema may bedeveloped and redefined as needed. Note that a flexible schema may beapplied to event data “on the fly,” when it is needed (e.g., at searchtime, index time, ingestion time, etc.). When the schema is not appliedto event data until search time it may be referred to as a “late-bindingschema.”

During operation, the SPLUNK® ENTERPRISE system starts with raw inputdata (e.g., one or more system logs, streams of network packet data,sensor data, application program data, error logs, stack traces, systemperformance data, etc.). The system divides this raw data into blocks(e.g., buckets of data, each associated with a specific time frame,etc.), and parses the raw data to produce timestamped events. The systemstores the timestamped events in a data store. The system enables usersto run queries against the stored data to, for example, retrieve eventsthat meet criteria specified in a query, such as containing certainkeywords or having specific values in defined fields. As used hereinthroughout, data that is part of an event is referred to as “eventdata”. In this context, the term “field” refers to a location in theevent data containing one or more values for a specific data item. Aswill be described in more detail herein, the fields are defined byextraction rules (e.g., regular expressions) that derive one or morevalues from the portion of raw machine data in each event that has aparticular field specified by an extraction rule. The set of values soproduced are semantically-related (such as by IP address), even thoughthe raw machine data in each event may be in different formats (e.g.,semantically-related values may be in different positions in the eventsderived from different sources).

As noted above, the SPLUNK® ENTERPRISE system utilizes a late-bindingschema to event data while performing queries on events. One aspect of alate-binding schema is applying “extraction rules” to event data toextract values for specific fields during search time. Morespecifically, the extraction rules for a field can include one or moreinstructions that specify how to extract a value for the field from theevent data. An extraction rule can generally include any type ofinstruction for extracting values from data in events. In some cases, anextraction rule comprises a regular expression where a sequence ofcharacters form a search pattern, in which case the rule is referred toas a “regex rule.” The system applies the regex rule to the event datato extract values for associated fields in the event data by searchingthe event data for the sequence of characters defined in the regex rule.

In the SPLUNK® ENTERPRISE system, a field extractor may be configured toautomatically generate extraction rules for certain field values in theevents when the events are being created, indexed, or stored, orpossibly at a later time. Alternatively, a user may manually defineextraction rules for fields using a variety of techniques. In contrastto a conventional schema for a database system, a late-binding schema isnot defined at data ingestion time. Instead, the late-binding schema canbe developed on an ongoing basis until the time a query is actuallyexecuted. This means that extraction rules for the fields in a query maybe provided in the query itself, or may be located during execution ofthe query. Hence, as a user learns more about the data in the events,the user can continue to refine the late-binding schema by adding newfields, deleting fields, or modifying the field extraction rules for usethe next time the schema is used by the system. Because the SPLUNK®ENTERPRISE system maintains the underlying raw data and useslate-binding schema for searching the raw data, it enables a user tocontinue investigating and learn valuable insights about the raw data.

In some embodiments, a common field name may be used to reference two ormore fields containing equivalent data items, even though the fields maybe associated with different types of events that possibly havedifferent data formats and different extraction rules. By enabling acommon field name to be used to identify equivalent fields fromdifferent types of events generated by disparate data sources, thesystem facilitates use of a “common information model” (CIM) across thedisparate data sources (further discussed with respect to FIG. 5 ).

2.0. OPERATING ENVIRONMENT

FIG. 1 illustrates a networked computer system 100 in which anembodiment may be implemented. Those skilled in the art would understandthat FIG. 1 represents one example of a networked computer system andother embodiments may use different arrangements.

The networked computer system 100 comprises one or more computingdevices. These one or more computing devices comprise any combination ofhardware and software configured to implement the various logicalcomponents described herein. For example, the one or more computingdevices may include one or more memories that store instructions forimplementing the various components described herein, one or morehardware processors configured to execute the instructions stored in theone or more memories, and various data repositories in the one or morememories for storing data structures utilized and manipulated by thevarious components.

In an embodiment, one or more client devices 102 are coupled to one ormore host devices 106 and a data intake and query system 108 via one ormore networks 104. Networks 104 broadly represent one or more local areanetworks (LANs), wide area networks (WANs), cellular networks (e.g.,Long-Term Evolution (LTE), High Speed Packet Access (HSPA), 3G, andother cellular technologies), and/or networks using any of wired,wireless, terrestrial microwave, or satellite links, and may include thepublic Internet.

2.1. Host Devices

In the illustrated embodiment, a system 100 includes one or more hostdevices 106. Host devices 106 may broadly include any number ofcomputers, virtual machine instances, and/or data centers that areconfigured to host or execute one or more instances of host applications114. In general, a host device 106 may be involved, directly orindirectly, in processing requests received from client devices 102.Each host device 106 may comprise, for example, one or more of a networkdevice, a web server, an application server, a database server, etc. Acollection of host devices 106 may be configured to implement anetwork-based service. For example, a provider of a network-basedservice may configure one or more host devices 106 and host applications114 (e.g., one or more web servers, application servers, databaseservers, etc.) to collectively implement the network-based application.

In general, client devices 102 communicate with one or more hostapplications 114 to exchange information. The communication between aclient device 102 and a host application 114 may, for example, be basedon the Hypertext Transfer Protocol (HTTP) or any other network protocol.Content delivered from the host application 114 to a client device 102may include, for example, HyperText Markup Language (HTML) documents,media content, etc. The communication between a client device 102 andhost application 114 may include sending various requests and receivingdata packets. For example, in general, a client device 102 orapplication running on a client device may initiate communication with ahost application 114 by making a request for a specific resource (e.g.,based on an HTTP request), and the application server may respond withthe requested content stored in one or more response packets.

In the illustrated embodiment, one or more of host applications 114 maygenerate various types of performance data during operation, includingevent logs, network data, sensor data, and other types ofmachine-generated data. For example, a host application 114 comprising aweb server may generate one or more web server logs in which details ofinteractions between the web server and any number of client devices 102is recorded. As another example, a host device 106 comprising a routermay generate one or more router logs that record information related tonetwork traffic managed by the router. As yet another example, a hostapplication 114 comprising a database server may generate one or morelogs that record information related to requests sent from other hostapplications 114 (e.g., web servers or application servers) for datamanaged by the database server.

2.2. Client Devices

Client devices 102 of FIG. 1 represent any computing device capable ofinteracting with one or more host devices 106 via a network 104.Examples of client devices 102 may include, without limitation, smartphones, tablet computers, handheld computers, wearable devices, laptopcomputers, desktop computers, servers, portable media players, gamingdevices, and so forth. In general, a client device 102 can provideaccess to different content, for instance, content provided by one ormore host devices 106, etc. Each client device 102 may comprise one ormore client applications 110, described in more detail in a separatesection hereinafter.

2.3. Client Device Applications

In an embodiment, each client device 102 may host or execute one or moreclient applications 110 that are capable of interacting with one or morehost devices 106 via one or more networks 104. For instance, a clientapplication 110 may be or comprise a web browser that a user may use tonavigate to one or more websites or other resources provided by one ormore host devices 106. As another example, a client application 110 maycomprise a mobile application or “app.” For example, an operator of anetwork-based service hosted by one or more host devices 106 may makeavailable one or more mobile apps that enable users of client devices102 to access various resources of the network-based service. As yetanother example, client applications 110 may include backgroundprocesses that perform various operations without direct interactionfrom a user. A client application 110 may include a “plug-in” or“extension” to another application, such as a web browser plug-in orextension.

In an embodiment, a client application 110 may include a monitoringcomponent 112. At a high level, the monitoring component 112 comprises asoftware component or other logic that facilitates generatingperformance data related to a client device's operating state, includingmonitoring network traffic sent and received from the client device andcollecting other device and/or application-specific information.Monitoring component 112 may be an integrated component of a clientapplication 110, a plug-in, an extension, or any other type of add-oncomponent. Monitoring component 112 may also be a stand-alone process.

In one embodiment, a monitoring component 112 may be created when aclient application 110 is developed, for example, by an applicationdeveloper using a software development kit (SDK). The SDK may includecustom monitoring code that can be incorporated into the codeimplementing a client application 110. When the code is converted to anexecutable application, the custom code implementing the monitoringfunctionality can become part of the application itself.

In some cases, an SDK or other code for implementing the monitoringfunctionality may be offered by a provider of a data intake and querysystem, such as a system 108. In such cases, the provider of the system108 can implement the custom code so that performance data generated bythe monitoring functionality is sent to the system 108 to facilitateanalysis of the performance data by a developer of the clientapplication or other users.

In an embodiment, the custom monitoring code may be incorporated intothe code of a client application 110 in a number of different ways, suchas the insertion of one or more lines in the client application codethat call or otherwise invoke the monitoring component 112. As such, adeveloper of a client application 110 can add one or more lines of codeinto the client application 110 to trigger the monitoring component 112at desired points during execution of the application. Code thattriggers the monitoring component may be referred to as a monitortrigger. For instance, a monitor trigger may be included at or near thebeginning of the executable code of the client application 110 such thatthe monitoring component 112 is initiated or triggered as theapplication is launched, or included at other points in the code thatcorrespond to various actions of the client application, such as sendinga network request or displaying a particular interface.

In an embodiment, the monitoring component 112 may monitor one or moreaspects of network traffic sent and/or received by a client application110. For example, the monitoring component 112 may be configured tomonitor data packets transmitted to and/or from one or more hostapplications 114. Incoming and/or outgoing data packets can be read orexamined to identify network data contained within the packets, forexample, and other aspects of data packets can be analyzed to determinea number of network performance statistics. Monitoring network trafficmay enable information to be gathered particular to the networkperformance associated with a client application 110 or set ofapplications.

In an embodiment, network performance data refers to any type of datathat indicates information about the network and/or network performance.Network performance data may include, for instance, a Uniform ResourceLocator (URL) requested, a connection type (e.g., HTTP, HTTP Secure(HTTPS), etc.), a connection start time, a connection end time, an HTTPstatus code, request length, response length, request headers, responseheaders, connection status (e.g., completion, response time(s), failure,etc.), and the like. Upon obtaining network performance data indicatingperformance of the network, the network performance data can betransmitted to a data intake and query system 108 for analysis.

Upon developing a client application 110 that incorporates a monitoringcomponent 112, the client application 110 can be distributed to clientdevices 102. Applications generally can be distributed to client devices102 in any manner, or they can be pre-loaded. In some cases, theapplication may be distributed to a client device 102 via an applicationmarketplace or other application distribution system. For instance, anapplication marketplace or other application distribution system mightdistribute the application to a client device based on a request fromthe client device to download the application.

Examples of functionality that enables monitoring performance of aclient device are described in U.S. patent application Ser. No.14/524,748, entitled “UTILIZING PACKET HEADERS TO MONITOR NETWORKTRAFFIC IN ASSOCIATION WITH A CLIENT DEVICE”, filed on 27 Oct. 2014, andwhich is hereby incorporated by reference in its entirety for allpurposes.

In an embodiment, the monitoring component 112 may also monitor andcollect performance data related to one or more aspects of theoperational state of a client application 110 and/or client device 102.For example, a monitoring component 112 may be configured to collectdevice performance information by monitoring one or more client deviceoperations, or by making calls to an operating system and/or one or moreother applications executing on a client device 102 for performanceinformation. Device performance information may include, for instance, acurrent wireless signal strength of the device, a current connectiontype and network carrier, current memory performance information, ageographic location of the device, a device orientation, and any otherinformation related to the operational state of the client device.

In an embodiment, the monitoring component 112 may also monitor andcollect other device profile information including, for example, a typeof client device, a manufacturer and model of the device, versions ofvarious software applications installed on the device, and so forth.

In general, a monitoring component 112 may be configured to generateperformance data in response to a monitor trigger in the code of aclient application 110 or other triggering application event, asdescribed above, and to store the performance data in one or more datarecords. Each data record, for example, may include a collection offield-value pairs, each field-value pair storing a particular item ofperformance data in association with a field for the item. For example,a data record generated by a monitoring component 112 may include a“networkLatency” field (not shown in the Figure) in which a value isstored. This field indicates a network latency measurement associatedwith one or more network requests. The data record may include a “state”field to store a value indicating a state of a network connection, andso forth for any number of aspects of collected performance data.

2.4. Data Server System

FIG. 2 depicts a block diagram of an exemplary data intake and querysystem 108, similar to the SPLUNK® ENTERPRISE system. System 108includes one or more forwarders 204 that receive data from a variety ofinput data sources 202, and one or more indexers 206 that process andstore the data in one or more data stores 208. These forwarders andindexers can comprise separate computer systems, or may alternativelycomprise separate processes executing on one or more computer systems.

Each data source 202 broadly represents a distinct source of data thatcan be consumed by a system 108. Examples of a data source 202 include,without limitation, data files, directories of files, data sent over anetwork, event logs, registries, etc.

During operation, the forwarders 204 identify which indexers 206 receivedata collected from a data source 202 and forward the data to theappropriate indexers. Forwarders 204 can also perform operations on thedata before forwarding, including removing extraneous data, detectingtimestamps in the data, parsing data, indexing data, routing data basedon criteria relating to the data being routed, and/or performing otherdata transformations.

In an embodiment, a forwarder 204 may comprise a service accessible toclient devices 102 and host devices 106 via a network 104. For example,one type of forwarder 204 may be capable of consuming vast amounts ofreal-time data from a potentially large number of client devices 102and/or host devices 106. The forwarder 204 may, for example, comprise acomputing device which implements multiple data pipelines or “queues” tohandle forwarding of network data to indexers 206. A forwarder 204 mayalso perform many of the functions that are performed by an indexer. Forexample, a forwarder 204 may perform keyword extractions on raw data orparse raw data to create events. A forwarder 204 may generate timestamps for events. Additionally or alternatively, a forwarder 204 mayperform routing of events to indexers. Data store 208 may contain eventsderived from machine data from a variety of sources all pertaining tothe same component in an IT environment, and this data may be producedby the machine in question or by other components in the IT environment.

2.5. Data Ingestion

FIG. 3 depicts a flow chart illustrating an example data flow performedby data intake and query system 108, in accordance with the disclosedembodiments. The data flow illustrated in FIG. 3 is provided forillustrative purposes only; those skilled in the art would understandthat one or more of the steps of the processes illustrated in FIG. 3 maybe removed or the ordering of the steps may be changed. Furthermore, forthe purposes of illustrating a clear example, one or more particularsystem components are described in the context of performing variousoperations during each of the data flow stages. For example, a forwarderis described as receiving and processing data during an input phase; anindexer is described as parsing and indexing data during parsing andindexing phases; and a search head is described as performing a searchquery during a search phase. However, other system arrangements anddistributions of the processing steps across system components may beused.

2.5.1. Input

At block 302, a forwarder receives data from an input source, such as adata source 202 shown in FIG. 2 . A forwarder initially may receive thedata as a raw data stream generated by the input source. For example, aforwarder may receive a data stream from a log file generated by anapplication server, from a stream of network data from a network device,or from any other source of data. In one embodiment, a forwarderreceives the raw data and may segment the data stream into “blocks”, or“buckets,” possibly of a uniform data size, to facilitate subsequentprocessing steps.

At block 304, a forwarder or other system component annotates each blockgenerated from the raw data with one or more metadata fields. Thesemetadata fields may, for example, provide information related to thedata block as a whole and may apply to each event that is subsequentlyderived from the data in the data block. For example, the metadatafields may include separate fields specifying each of a host, a source,and a source type related to the data block. A host field may contain avalue identifying a host name or Internet Protocol (IP) address of adevice that generated the data. A source field may contain a valueidentifying a source of the data, such as a pathname of a file or aprotocol and port related to received network data. A source type fieldmay contain a value specifying a particular source type label for thedata. Additional metadata fields may also be included during the inputphase, such as a character encoding of the data, if known, and possiblyother values that provide information relevant to later processingsteps. In an embodiment, a forwarder forwards the annotated data blocksto another system component (typically an indexer) for furtherprocessing.

The SPLUNK® ENTERPRISE system allows forwarding of data from one SPLUNK®ENTERPRISE instance to another, or even to a third-party system. SPLUNK®ENTERPRISE system can employ different types of forwarders in aconfiguration.

In an embodiment, a forwarder may contain the essential componentsneeded to forward data. It can gather data from a variety of inputs andforward the data to a SPLUNK® ENTERPRISE server for indexing andsearching. It also can tag metadata (e.g., source, source type, host,etc.).

Additionally or optionally, in an embodiment, a forwarder has thecapabilities of the aforementioned forwarder as well as additionalcapabilities. The forwarder can parse data before forwarding the data(e.g., associate a time stamp with a portion of data and create anevent, etc.) and can route data based on criteria such as source or typeof event. It can also index data locally while forwarding the data toanother indexer.

2.5.2. Parsing

At block 306, an indexer receives data blocks from a forwarder andparses the data to organize the data into events. In an embodiment, toorganize the data into events, an indexer may determine a source typeassociated with each data block (e.g., by extracting a source type labelfrom the metadata fields associated with the data block, etc.) and referto a source type configuration corresponding to the identified sourcetype. The source type definition may include one or more properties thatindicate to the indexer to automatically determine the boundaries ofevents within the data. In general, these properties may include regularexpression-based rules or delimiter rules where, for example, eventboundaries may be indicated by predefined characters or characterstrings. These predefined characters may include punctuation marks orother special characters including, for example, carriage returns, tabs,spaces, line breaks, etc. If a source type for the data is unknown tothe indexer, an indexer may infer a source type for the data byexamining the structure of the data. Then, it can apply an inferredsource type definition to the data to create the events.

At block 308, the indexer determines a timestamp for each event. Similarto the process for creating events, an indexer may again refer to asource type definition associated with the data to locate one or moreproperties that indicate instructions for determining a timestamp foreach event. The properties may, for example, instruct an indexer toextract a time value from a portion of data in the event, to interpolatetime values based on timestamps associated with temporally proximateevents, to create a timestamp based on a time the event data wasreceived or generated, to use the timestamp of a previous event, or useany other rules for determining timestamps.

At block 310, the indexer associates with each event one or moremetadata fields including a field containing the timestamp (in someembodiments, a timestamp may be included in the metadata fields)determined for the event. These metadata fields may include a number of“default fields” that are associated with all events, and may alsoinclude one more custom fields as defined by a user. Similar to themetadata fields associated with the data blocks at block 304, thedefault metadata fields associated with each event may include a host,source, and source type field including or in addition to a fieldstoring the timestamp.

At block 312, an indexer may optionally apply one or moretransformations to data included in the events created at block 306. Forexample, such transformations can include removing a portion of an event(e.g., a portion used to define event boundaries, extraneous charactersfrom the event, other extraneous text, etc.), masking a portion of anevent (e.g., masking a credit card number), removing redundant portionsof an event, etc. The transformations applied to event data may, forexample, be specified in one or more configuration files and referencedby one or more source type definitions.

2.5.3. Indexing

At blocks 314 and 316, an indexer can optionally generate a keywordindex to facilitate fast keyword searching for event data. To build akeyword index, at block 314, the indexer identifies a set of keywords ineach event. At block 316, the indexer includes the identified keywordsin an index, which associates each stored keyword with referencepointers to events containing that keyword (or to locations withinevents where that keyword is located, other location identifiers, etc.).When an indexer subsequently receives a keyword-based query, the indexercan access the keyword index to quickly identify events containing thekeyword.

In some embodiments, the keyword index may include entries forname-value pairs found in events, where a name-value pair can include apair of keywords connected by a symbol, such as an equals sign or colon.This way, events containing these name-value pairs can be quicklylocated. In some embodiments, fields can automatically be generated forsome or all of the name-value pairs at the time of indexing. Forexample, if the string “dest=10.0.1.2” is found in an event, a fieldnamed “dest” may be created for the event, and assigned a value of“10.0.1.2”.

At block 318, the indexer stores the events with an associated timestampin a data store 208. Timestamps enable a user to search for events basedon a time range. In one embodiment, the stored events are organized into“buckets,” where each bucket stores events associated with a specifictime range based on the timestamps associated with each event. This maynot only improve time-based searching, but also allows for events withrecent timestamps, which may have a higher likelihood of being accessed,to be stored in a faster memory to facilitate faster retrieval. Forexample, buckets containing the most recent events can be stored inflash memory rather than on a hard disk.

Each indexer 206 may be responsible for storing and searching a subsetof the events contained in a corresponding data store 208. Bydistributing events among the indexers and data stores, the indexers cananalyze events for a query in parallel. For example, using map-reducetechniques, each indexer returns partial responses for a subset ofevents to a search head that combines the results to produce an answerfor the query. By storing events in buckets for specific time ranges, anindexer may further optimize data retrieval process by searching bucketscorresponding to time ranges that are relevant to a query.

Moreover, events and buckets can also be replicated across differentindexers and data stores to facilitate high availability and disasterrecovery as described in U.S. patent application Ser. No. 14/266,812,entitled “SITE-BASED SEARCH AFFINITY”, filed on 30 Apr. 2014, and inU.S. patent application Ser. No. 14/266,817, entitled “MULTI-SITECLUSTERING”. also filed on 30 Apr. 2014, each of which is herebyincorporated by reference in its entirety for all purposes.

2.6. Query Processing

FIG. 4 is a flow diagram that illustrates an exemplary process that asearch head and one or more indexers may perform during a search query.At block 402, a search head receives a search query from a client. Atblock 404, the search head analyzes the search query to determine whatportion(s) of the query can be delegated to indexers and what portionsof the query can be executed locally by the search head. At block 406,the search head distributes the determined portions of the query to theappropriate indexers. In an embodiment, a search head cluster may takethe place of an independent search head where each search head in thesearch head cluster coordinates with peer search heads in the searchhead cluster to schedule jobs, replicate search results, updateconfigurations, fulfill search requests, etc. In an embodiment, thesearch head (or each search head) communicates with a master node (alsoknown as a cluster master, not shown in FIG. 4 ) that provides thesearch head with a list of indexers to which the search head candistribute the determined portions of the query. The master nodemaintains a list of active indexers and can also designate whichindexers may have responsibility for responding to queries over certainsets of events. A search head may communicate with the master nodebefore the search head distributes queries to indexers to discover theaddresses of active indexers.

At block 408, the indexers to which the query was distributed, searchdata stores associated with them for events that are responsive to thequery. To determine which events are responsive to the query, theindexer searches for events that match the criteria specified in thequery. These criteria can include matching keywords or specific valuesfor certain fields. The searching operations at block 408 may use thelate-binding schema to extract values for specified fields from eventsat the time the query is processed. In an embodiment, one or more rulesfor extracting field values may be specified as part of a source typedefinition. The indexers may then either send the relevant events backto the search head, or use the events to determine a partial result, andsend the partial result back to the search head.

At block 410, the search head combines the partial results and/or eventsreceived from the indexers to produce a final result for the query. Thisfinal result may comprise different types of data depending on what thequery requested. For example, the results can include a listing ofmatching events returned by the query, or some type of visualization ofthe data from the returned events. In another example, the final resultcan include one or more calculated values derived from the matchingevents.

The results generated by the system 108 can be returned to a clientusing different techniques. For example, one technique streams resultsor relevant events back to a client in real-time as they are identified.Another technique waits to report the results to the client until acomplete set of results (which may include a set of relevant events or aresult based on relevant events) is ready to return to the client. Yetanother technique streams interim results or relevant events back to theclient in real-time until a complete set of results is ready, and thenreturns the complete set of results to the client. In another technique,certain results are stored as “search jobs” and the client may retrievethe results by referring the search jobs.

The search head can also perform various operations to make the searchmore efficient. For example, before the search head begins execution ofa query, the search head can determine a time range for the query and aset of common keywords that all matching events include. The search headmay then use these parameters to query the indexers to obtain a supersetof the eventual results. Then, during a filtering stage, the search headcan perform field-extraction operations on the superset to produce areduced set of search results. This speeds up queries that are performedon a periodic basis.

2.7. Field Extraction

The search head 210 allows users to search and visualize event dataextracted from raw machine data received from homogenous data sources.It also allows users to search and visualize event data extracted fromraw machine data received from heterogeneous data sources. The searchhead 210 includes various mechanisms, which may additionally reside inan indexer 206, for processing a query. Splunk Processing Language(SPL), used in conjunction with the SPLUNK® ENTERPRISE system, can beutilized to make a query. SPL is a pipelined search language in which aset of inputs is operated on by a first command in a command line, andthen a subsequent command following the pipe symbol “|” operates on theresults produced by the first command, and so on for additionalcommands. Other query languages, such as the Structured Query Language(“SQL”), can be used to create a query.

In response to receiving the search query, search head 210 usesextraction rules to extract values for the fields associated with afield or fields in the event data being searched. The search head 210obtains extraction rules that specify how to extract a value for certainfields from an event. Extraction rules can comprise regex rules thatspecify how to extract values for the relevant fields. In addition tospecifying how to extract field values, the extraction rules may alsoinclude instructions for deriving a field value by performing a functionon a character string or value retrieved by the extraction rule. Forexample, a transformation rule may truncate a character string, orconvert the character string into a different data format. In somecases, the query itself can specify one or more extraction rules.

The search head 210 can apply the extraction rules to event data that itreceives from indexers 206. Indexers 206 may apply the extraction rulesto events in an associated data store 208. Extraction rules can beapplied to all the events in a data store, or to a subset of the eventsthat have been filtered based on some criteria (e.g., event time stampvalues, etc.). Extraction rules can be used to extract one or morevalues for a field from events by parsing the event data and examiningthe event data for one or more patterns of characters, numbers,delimiters, etc., that indicate where the field begins and, optionally,ends.

FIG. 5 illustrates an example of raw machine data received fromdisparate data sources. In this example, a user submits an order formerchandise using a vendor's shopping application 501 running on theuser's system. In this example, the order was not delivered to thevendor's server due to a resource exception at the destination serverthat is detected by the middleware code 502. The user then sends amessage to the customer support server 503 to complain about the orderfailing to complete. The three systems 501, 502, and 503 are disparatesystems that do not have a common logging format. The order application501 sends log data 504 to the SPLUNK® ENTERPRISE system in one format,the middleware code 502 sends error log data 505 in a second format, andthe support server 503 sends log data 506 in a third format.

Using the log data received at one or more indexers 206 from the threesystems the vendor can uniquely obtain an insight into user activity,user experience, and system behavior. The search head 210 allows thevendor's administrator to search the log data from the three systemsthat one or more indexers 206 are responsible for searching, therebyobtaining correlated information, such as the order number andcorresponding customer ID number of the person placing the order. Thesystem also allows the administrator to see a visualization of relatedevents via a user interface. The administrator can query the search head210 for customer ID field value matches across the log data from thethree systems that are stored at the one or more indexers 206. Thecustomer ID field value exists in the data gathered from the threesystems, but the customer ID field value may be located in differentareas of the data given differences in the architecture of thesystems—there is a semantic relationship between the customer ID fieldvalues generated by the three systems. The search head 210 requestsevent data from the one or more indexers 206 to gather relevant eventdata from the three systems. It then applies extraction rules to theevent data in order to extract field values that it can correlate. Thesearch head may apply a different extraction rule to each set of eventsfrom each system when the event data format differs among systems. Inthis example, the user interface can display to the administrator theevent data corresponding to the common customer ID field values 507,508, and 509, thereby providing the administrator with insight into acustomer's experience.

Note that query results can be returned to a client, a search head, orany other system component for further processing. In general, queryresults may include a set of one or more events, a set of one or morevalues obtained from the events, a subset of the values, statisticscalculated based on the values, a report containing the values, or avisualization, such as a graph or chart, generated from the values.

2.8. Example Search Screen

FIG. 6A illustrates an example search screen 600 in accordance with thedisclosed embodiments. Search screen 600 includes a search bar 602 thataccepts user input in the form of a search string. It also includes atime range picker 612 that enables the user to specify a time range forthe search. For “historical searches” the user can select a specifictime range, or alternatively a relative time range, such as “today,”“yesterday” or “last week.” For “real-time searches,” the user canselect the size of a preceding time window to search for real-timeevents. Search screen 600 also initially displays a “data summary”dialog as is illustrated in FIG. 6B that enables the user to selectdifferent sources for the event data, such as by selecting specifichosts and log files.

After the search is executed, the search screen 600 in FIG. 6A candisplay the results through search results tabs 604, wherein searchresults tabs 604 includes: an “events tab” that displays variousinformation about events returned by the search; a “statistics tab” thatdisplays statistics about the search results; and a “visualization tab”that displays various visualizations of the search results. The eventstab illustrated in FIG. 6A displays a timeline graph 605 thatgraphically illustrates the number of events that occurred in one-hourintervals over the selected time range. It also displays an events list608 that enables a user to view the raw data in each of the returnedevents. It additionally displays a fields sidebar 606 that includesstatistics about occurrences of specific fields in the returned events,including “selected fields” that are pre-selected by the user, and“interesting fields” that are automatically selected by the system basedon pre-specified criteria.

2.9. Data Models

A data model is a hierarchically structured search-time mapping ofsemantic knowledge about one or more datasets. It encodes the domainknowledge necessary to build a variety of specialized searches of thosedatasets. Those searches, in turn, can be used to generate reports.

A data model is composed of one or more “objects” (or “data modelobjects”) that define or otherwise correspond to a specific set of data.

Objects in data models can be arranged hierarchically in parent/childrelationships. Each child object represents a subset of the datasetcovered by its parent object. The top-level objects in data models arecollectively referred to as “root objects.”

Child objects have inheritance. Data model objects are defined bycharacteristics that mostly break down into constraints and attributes.Child objects inherit constraints and attributes from their parentobjects and have additional constraints and attributes of their own.Child objects provide a way of filtering events from parent objects.Because a child object always provides an additional constraint inaddition to the constraints it has inherited from its parent object, thedataset it represents is always a subset of the dataset that its parentrepresents.

For example, a first data model object may define a broad set of datapertaining to e-mail activity generally, and another data model objectmay define specific datasets within the broad dataset, such as a subsetof the e-mail data pertaining specifically to e-mails sent. Examples ofdata models can include electronic mail, authentication, databases,intrusion detection, malware, application state, alerts, computeinventory, network sessions, network traffic, performance, audits,updates, vulnerabilities, etc. Data models and their objects can bedesigned by knowledge managers in an organization, and they can enabledownstream users to quickly focus on a specific set of data. Forexample, a user can simply select an “e-mail activity” data model objectto access a dataset relating to e-mails generally (e.g., sent orreceived), or select an “e-mails sent” data model object (or datasub-model object) to access a dataset relating to e-mails sent.

A data model object may be defined by (1) a set of search constraints,and (2) a set of fields. Thus, a data model object can be used toquickly search data to identify a set of events and to identify a set offields to be associated with the set of events. For example, an “e-mailssent” data model object may specify a search for events relating toe-mails that have been sent, and specify a set of fields that areassociated with the events. Thus, a user can retrieve and use the“e-mails sent” data model object to quickly search source data forevents relating to sent e-mails, and may be provided with a listing ofthe set of fields relevant to the events in a user interface screen.

A child of the parent data model may be defined by a search (typically anarrower search) that produces a subset of the events that would beproduced by the parent data model's search. The child's set of fieldscan include a subset of the set of fields of the parent data modeland/or additional fields. Data model objects that reference the subsetscan be arranged in a hierarchical manner, so that child subsets ofevents are proper subsets of their parents. A user iteratively applies amodel development tool (not shown in Fig.) to prepare a query thatdefines a subset of events and assigns an object name to that subset. Achild subset is created by further limiting a query that generated aparent subset. A late-binding schema of field extraction rules isassociated with each object or subset in the data model.

Data definitions in associated schemas can be taken from the commoninformation model (CIM) or can be devised for a particular schema andoptionally added to the CIM. Child objects inherit fields from parentsand can include fields not present in parents. A model developer canselect fewer extraction rules than are available for the sourcesreturned by the query that defines events belonging to a model.Selecting a limited set of extraction rules can be a tool forsimplifying and focusing the data model, while allowing a userflexibility to explore the data subset. Development of a data model isfurther explained in U.S. Pat. Nos. 8,788,525 and 8,788,526, bothentitled “DATA MODEL FOR MACHINE DATA FOR SEMANTIC SEARCH”, both issuedon 22 Jul. 2014, U.S. Pat. No. 8,983,994, entitled “GENERATION OF A DATAMODEL FOR SEARCHING MACHINE DATA”, issued on 17 Mar. 2015, U.S. patentapplication Ser. No. 14/611,232, entitled “GENERATION OF A DATA MODELAPPLIED TO QUERIES”, filed on 31 Jan. 2015, and U.S. patent applicationSer. No. 14/815,884, entitled “GENERATION OF A DATA MODEL APPLIED TOOBJECT QUERIES”, filed on 31 Jul. 2015, each of which is herebyincorporated by reference in its entirety for all purposes. See, also,Knowledge Manager Manual, Build a Data Model, Splunk Enterprise 6.1.3pp. 150-204 (Aug. 25, 2014).

A data model can also include reports. One or more report formats can beassociated with a particular data model and be made available to runagainst the data model. A user can use child objects to design reportswith object datasets that already have extraneous data pre-filtered out.In an embodiment, the data intake and query system 108 provides the userwith the ability to produce reports (e.g., a table, chart,visualization, etc.) without having to enter SPL, SQL, or other querylanguage terms into a search screen. Data models are used as the basisfor the search feature.

Data models may be selected in a report generation interface. The reportgenerator supports drag-and-drop organization of fields to be summarizedin a report. When a model is selected, the fields with availableextraction rules are made available for use in the report. The user mayrefine and/or filter search results to produce more precise reports. Theuser may select some fields for organizing the report and select otherfields for providing detail according to the report organization. Forexample, “region” and “salesperson” are fields used for organizing thereport and sales data can be summarized (subtotaled and totaled) withinthis organization. The report generator allows the user to specify oneor more fields within events and apply statistical analysis on valuesextracted from the specified one or more fields. The report generatormay aggregate search results across sets of events and generatestatistics based on aggregated search results. Building reports usingthe report generation interface is further explained in U.S. patentapplication Ser. No. 14/503,335, entitled “GENERATING REPORTS FROMUNSTRUCTURED DATA”, filed on 30 Sep. 2014, and which is herebyincorporated by reference in its entirety for all purposes, and in PivotManual, Splunk Enterprise 6.1.3 (Aug. 4, 2014). Data visualizations alsocan be generated in a variety of formats, by reference to the datamodel. Reports, data visualizations, and data model objects can be savedand associated with the data model for future use. The data model objectmay be used to perform searches of other data.

FIGS. 12, 13, and 7A-7D illustrate a series of user interface screenswhere a user may select report generation options using data models. Thereport generation process may be driven by a predefined data modelobject, such as a data model object defined and/or saved via a reportingapplication or a data model object obtained from another source. A usercan load a saved data model object using a report editor. For example,the initial search query and fields used to drive the report editor maybe obtained from a data model object. The data model object that is usedto drive a report generation process may define a search and a set offields. Upon loading of the data model object, the report generationprocess may enable a user to use the fields (e.g., the fields defined bythe data model object) to define criteria for a report (e.g., filters,split rows/columns, aggregates, etc.) and the search may be used toidentify events (e.g., to identify events responsive to the search) usedto generate the report. That is, for example, if a data model object isselected to drive a report editor, the graphical user interface of thereport editor may enable a user to define reporting criteria for thereport using the fields associated with the selected data model object,and the events used to generate the report may be constrained to theevents that match, or otherwise satisfy, the search constraints of theselected data model object.

The selection of a data model object for use in driving a reportgeneration may be facilitated by a data model object selectioninterface. FIG. 12 illustrates an example interactive data modelselection graphical user interface 1200 of a report editor that displaysa listing of available data models 1201. The user may select one of thedata models 1202.

FIG. 13 illustrates an example data model object selection graphicaluser interface 1300 that displays available data objects 1301 for theselected data object model 1202. The user may select one of thedisplayed data model objects 1302 for use in driving the reportgeneration process.

Once a data model object is selected by the user, a user interfacescreen 700 shown in FIG. 7A may display an interactive listing ofautomatic field identification options 701 based on the selected datamodel object. For example, a user may select one of the threeillustrated options (e.g., the “All Fields” option 702, the “SelectedFields” option 703, or the “Coverage” option (e.g., fields with at leasta specified % of coverage) 704). If the user selects the “All Fields”option 702, all of the fields identified from the events that werereturned in response to an initial search query may be selected. Thatis, for example, all of the fields of the identified data model objectfields may be selected. If the user selects the “Selected Fields” option703, only the fields from the fields of the identified data model objectfields that are selected by the user may be used. If the user selectsthe “Coverage” option 704, only the fields of the identified data modelobject fields meeting a specified coverage criteria may be selected. Apercent coverage may refer to the percentage of events returned by theinitial search query that a given field appears in. Thus, for example,if an object dataset includes 10,000 events returned in response to aninitial search query, and the “avg_age” field appears in 854 of those10,000 events, then the “avg_age” field would have a coverage of 8.54%for that object dataset. If, for example, the user selects the“Coverage” option and specifies a coverage value of 2%, only fieldshaving a coverage value equal to or greater than 2% may be selected. Thenumber of fields corresponding to each selectable option may bedisplayed in association with each option. For example, “97” displayednext to the “All Fields” option 702 indicates that 97 fields will beselected if the “All Fields” option is selected. The “3” displayed nextto the “Selected Fields” option 703 indicates that 3 of the 97 fieldswill be selected if the “Selected Fields” option is selected. The “49”displayed next to the “Coverage” option 704 indicates that 49 of the 97fields (e.g., the 49 fields having a coverage of 2% or greater) will beselected if the “Coverage” option is selected. The number of fieldscorresponding to the “Coverage” option may be dynamically updated basedon the specified percent of coverage.

FIG. 7B illustrates an example graphical user interface screen (alsocalled the pivot interface) 705 displaying the reporting application's“Report Editor” page. The screen may display interactive elements fordefining various elements of a report. For example, the page includes a“Filters” element 706, a “Split Rows” element 707, a “Split Columns”element 708, and a “Column Values” element 709. The page may include alist of search results 711. In this example, the Split Rows element 707is expanded, revealing a listing of fields 710 that can be used todefine additional criteria (e.g., reporting criteria). The listing offields 710 may correspond to the selected fields (attributes). That is,the listing of fields 710 may list only the fields previously selected,either automatically and/or manually by a user. FIG. 7C illustrates aformatting dialogue 712 that may be displayed upon selecting a fieldfrom the listing of fields 710. The dialogue can be used to format thedisplay of the results of the selection (e.g., to label the column to bedisplayed as “optional”).

FIG. 7D illustrates an example graphical user interface screen 705including a table of results 713 based on the selected criteriaincluding splitting the rows by the “component” field. A column 714having an associated count for each component listed in the table may bedisplayed that indicates an aggregate count of the number of times thatthe particular field-value pair (e.g., the value in a row) occurs in theset of events responsive to the initial search query.

FIG. 14 illustrates an example graphical user interface screen 1400 thatallows the user to filter search results and to perform statisticalanalysis on values extracted from specific fields in the set of events.In this example, the top ten product names ranked by price are selectedas a filter 1401 that causes the display of the ten most popularproducts sorted by price. Each row is displayed by product name andprice 1402. This results in each product displayed in a column labeled“product name” along with an associated price in a column labeled“price” 1406. Statistical analysis of other fields in the eventsassociated with the ten most popular products have been specified ascolumn values 1403. A count of the number of successful purchases foreach product is displayed in column 1404. These statistics may beproduced by filtering the search results by the product name, findingall occurrences of a successful purchase in a field within the eventsand generating a total of the number of occurrences. A sum of the totalsales is displayed in column 1405, which is a result of themultiplication of the price and the number of successful purchases foreach product.

The reporting application allows the user to create graphicalvisualizations of the statistics generated for a report. For example,FIG. 15 illustrates an example graphical user interface 1500 thatdisplays a set of components and associated statistical data 1501. Thereporting application allows the user to select a visualization of thestatistics in a graph (e.g., bar chart, scatter plot, area chart, linechart, pie chart, radial gauge, marker gauge, filler gauge, etc.). FIG.16 illustrates an example of a bar chart visualization 1600 of an aspectof the statistical data 1501. FIG. 17 illustrates a scatter plotvisualization 1700 of an aspect of the statistical data 1501.

2.10. Acceleration Technique

The above-described system provides significant flexibility by enablinga user to analyze massive quantities of minimally processed data “on thefly” at search time instead of storing pre-specified portions of thedata in a database at ingestion time. This flexibility enables a user tosee valuable insights, correlate data, and perform subsequent queries toexamine interesting aspects of the data that may not have been apparentat ingestion time.

However, performing extraction and analysis operations at search timecan involve a large amount of data and require a large number ofcomputational operations, which can cause delays in processing thequeries. Advantageously, the SPLUNK® ENTERPRISE system employs a numberof unique acceleration techniques that have been developed to speed upanalysis operations performed at search time. These techniques include:(1) performing search operations in parallel across multiple indexers;(2) using a keyword index; (3) using a high performance analytics store;and (4) accelerating the process of generating reports. These noveltechniques are described in more detail below.

2.10.1. Aggregation Technique

To facilitate faster query processing, a query can be structured suchthat multiple indexers perform the query in parallel, while aggregationof search results from the multiple indexers is performed locally at thesearch head. For example, FIG. 8 illustrates how a search query 802received from a client at a search head 210 can split into two phases,including: (1) subtasks 804 (e.g., data retrieval or simple filtering)that may be performed in parallel by indexers 206 for execution, and (2)a search results aggregation operation 806 to be executed by the searchhead when the results are ultimately collected from the indexers.

During operation, upon receiving search query 802, a search head 210determines that a portion of the operations involved with the searchquery may be performed locally by the search head. The search headmodifies search query 802 by substituting “stats” (create aggregatestatistics over results sets received from the indexers at the searchhead) with “prestats” (create statistics by the indexer from localresults set) to produce search query 804, and then distributes searchquery 804 to distributed indexers, which are also referred to as “searchpeers.” Note that search queries may generally specify search criteriaor operations to be performed on events that meet the search criteria.Search queries may also specify field names, as well as search criteriafor the values in the fields or operations to be performed on the valuesin the fields. Moreover, the search head may distribute the full searchquery to the search peers as illustrated in FIG. 4 , or mayalternatively distribute a modified version (e.g., a more restrictedversion) of the search query to the search peers. In this example, theindexers are responsible for producing the results and sending them tothe search head. After the indexers return the results to the searchhead, the search head aggregates the received results 806 to form asingle search result set. By executing the query in this manner, thesystem effectively distributes the computational operations across theindexers while minimizing data transfers.

2.10.2. Keyword Index

As described above with reference to the flow charts in FIG. 3 and FIG.4 , data intake and query system 108 can construct and maintain one ormore keyword indices to quickly identify events containing specifickeywords. This technique can greatly speed up the processing of queriesinvolving specific keywords. As mentioned above, to build a keywordindex, an indexer first identifies a set of keywords. Then, the indexerincludes the identified keywords in an index, which associates eachstored keyword with references to events containing that keyword, or tolocations within events where that keyword is located. When an indexersubsequently receives a keyword-based query, the indexer can access thekeyword index to quickly identify events containing the keyword.

2.10.3. High Performance Analytics Store

To speed up certain types of queries, some embodiments of system 108create a high performance analytics store, which is referred to as a“summarization table,” that contains entries for specific field-valuepairs. Each of these entries keeps track of instances of a specificvalue in a specific field in the event data and includes references toevents containing the specific value in the specific field. For example,an example entry in a summarization table can keep track of occurrencesof the value “94107” in a “ZIP code” field of a set of events and theentry includes references to all of the events that contain the value“94107” in the ZIP code field. This optimization technique enables thesystem to quickly process queries that seek to determine how many eventshave a particular value for a particular field. To this end, the systemcan examine the entry in the summarization table to count instances ofthe specific value in the field without having to go through theindividual events or perform data extractions at search time. Also, ifthe system needs to process all events that have a specific field-valuecombination, the system can use the references in the summarizationtable entry to directly access the events to extract further informationwithout having to search all of the events to find the specificfield-value combination at search time.

In some embodiments, the system maintains a separate summarization tablefor each of the above-described time-specific buckets that stores eventsfor a specific time range. A bucket-specific summarization tableincludes entries for specific field-value combinations that occur inevents in the specific bucket. Alternatively, the system can maintain aseparate summarization table for each indexer. The indexer-specificsummarization table includes entries for the events in a data store thatare managed by the specific indexer. Indexer-specific summarizationtables may also be bucket-specific.

The summarization table can be populated by running a periodic querythat scans a set of events to find instances of a specific field-valuecombination, or alternatively instances of all field-value combinationsfor a specific field. A periodic query can be initiated by a user, orcan be scheduled to occur automatically at specific time intervals. Aperiodic query can also be automatically launched in response to a querythat asks for a specific field-value combination.

In some cases, when the summarization tables may not cover all of theevents that are relevant to a query, the system can use thesummarization tables to obtain partial results for the events that arecovered by summarization tables, but may also have to search throughother events that are not covered by the summarization tables to produceadditional results. These additional results can then be combined withthe partial results to produce a final set of results for the query. Thesummarization table and associated techniques are described in moredetail in U.S. Pat. No. 8,682,925, entitled “DISTRIBUTED HIGHPERFORMANCE ANALYTICS STORE”, issued on 25 Mar. 2014, U.S. patentapplication Ser. No. 14/170,159, entitled “SUPPLEMENTING A HIGHPERFORMANCE ANALYTICS STORE WITH EVALUATION OF INDIVIDUAL EVENTS TORESPOND TO AN EVENT QUERY”, filed on 31 Jan. 2014, and U.S. patentapplication Ser. No. 14/815,973, entitled “STORAGE MEDIUM AND CONTROLDEVICE”, filed on 21 Feb. 2014, each of which is hereby incorporated byreference in its entirety.

2.10.4. Accelerating Report Generation

In some embodiments, a data server system such as the SPLUNK® ENTERPRISEsystem can accelerate the process of periodically generating updatedreports based on query results. To accelerate this process, asummarization engine automatically examines the query to determinewhether generation of updated reports can be accelerated by creatingintermediate summaries. If reports can be accelerated, the summarizationengine periodically generates a summary covering data obtained during alatest non-overlapping time period. For example, where the query seeksevents meeting a specified criteria, a summary for the time periodincludes only events within the time period that meet the specifiedcriteria. Similarly, if the query seeks statistics calculated from theevents, such as the number of events that match the specified criteria,then the summary for the time period includes the number of events inthe period that match the specified criteria.

In addition to the creation of the summaries, the summarization engineschedules the periodic updating of the report associated with the query.During each scheduled report update, the query engine determines whetherintermediate summaries have been generated covering portions of the timeperiod covered by the report update. If so, then the report is generatedbased on the information contained in the summaries. Also, if additionalevent data has been received and has not yet been summarized, and isrequired to generate the complete report, the query can be run on thisadditional event data. Then, the results returned by this query on theadditional event data, along with the partial results obtained from theintermediate summaries, can be combined to generate the updated report.This process is repeated each time the report is updated. Alternatively,if the system stores events in buckets covering specific time ranges,then the summaries can be generated on a bucket-by-bucket basis. Notethat producing intermediate summaries can save the work involved inre-running the query for previous time periods, so advantageously onlythe newer event data needs to be processed while generating an updatedreport. These report acceleration techniques are described in moredetail in U.S. Pat. No. 8,589,403, entitled “COMPRESSED JOURNALING INEVENT TRACKING FILES FOR METADATA RECOVERY AND REPLICATION”, issued on19 Nov. 2013, U.S. Pat. No. 8,412,696, entitled “REAL TIME SEARCHING ANDREPORTING”, issued on 2 Apr. 2011, and U.S. Pat. Nos. 8,589,375 and8,589,432, both also entitled “REAL TIME SEARCHING AND REPORTING”, bothissued on 19 Nov. 2013, each of which is hereby incorporated byreference in its entirety.

2.11. Security Features

The SPLUNK® ENTERPRISE platform provides various schemas, dashboards,and visualizations that simplify developers' task to create applicationswith additional capabilities. One such application is the SPLUNK® APPFOR ENTERPRISE SECURITY, which performs monitoring and alertingoperations and includes analytics to facilitate identifying both knownand unknown security threats based on large volumes of data stored bythe SPLUNK® ENTERPRISE system. SPLUNK® APP FOR ENTERPRISE SECURITYprovides the security practitioner with visibility intosecurity-relevant threats found in the enterprise infrastructure bycapturing, monitoring, and reporting on data from enterprise securitydevices, systems, and applications. Through the use of SPLUNK®ENTERPRISE searching and reporting capabilities, SPLUNK® APP FORENTERPRISE SECURITY provides a top-down and bottom-up view of anorganization's security posture.

The SPLUNK® APP FOR ENTERPRISE SECURITY leverages SPLUNK® ENTERPRISEsearch-time normalization techniques, saved searches, and correlationsearches to provide visibility into security-relevant threats andactivity and generate notable events for tracking. The applicationenables the security practitioner to investigate and explore the data tofind new or unknown threats that do not follow signature-based patterns.

Conventional Security Information and Event Management (SIEM) systemslack the infrastructure to effectively store and analyze large volumesof security-related data. Traditional SIEM systems typically use fixedschemas to extract data from pre-defined security-related fields at dataingestion time and store the extracted data in a relational database.This traditional data extraction process (and associated reduction indata size) that occurs at data ingestion time inevitably hampers futureincident investigations that may need original data to determine theroot cause of a security issue, or to detect the onset of an impendingsecurity threat.

In contrast, the SPLUNK® APP FOR ENTERPRISE SECURITY system stores largevolumes of minimally processed security-related data at ingestion timefor later retrieval and analysis at search time when a live securitythreat is being investigated. To facilitate this data retrieval process,the SPLUNK® APP FOR ENTERPRISE SECURITY provides pre-specified schemasfor extracting relevant values from the different types ofsecurity-related event data and enables a user to define such schemas.

The SPLUNK® APP FOR ENTERPRISE SECURITY can process many types ofsecurity-related information. In general, this security-relatedinformation can include any information that can be used to identifysecurity threats. For example, the security-related information caninclude network-related information, such as IP addresses, domain names,asset identifiers, network traffic volume, uniform resource locatorstrings, and source addresses. The process of detecting security threatsfor network-related information is further described in U.S. Pat. No.8,826,434, entitled “SECURITY THREAT DETECTION BASED ON INDICATIONS INBIG DATA OF ACCESS TO NEWLY REGISTERED DOMAINS”, issued on 2 Sep. 2014,U.S. patent application Ser. No. 13/956,252, entitled “INVESTIGATIVE ANDDYNAMIC DETECTION OF POTENTIAL SECURITY-THREAT INDICATORS FROM EVENTS INBIG DATA”, filed on 31 Jul. 2013, U.S. patent application Ser. No.14/445,018, entitled “GRAPHIC DISPLAY OF SECURITY THREATS BASED ONINDICATIONS OF ACCESS TO NEWLY REGISTERED DOMAINS”, filed on 28 Jul.2014, U.S. patent application Ser. No. 14/445,023, entitled “SECURITYTHREAT DETECTION OF NEWLY REGISTERED DOMAINS”, filed on 28 Jul. 2014,U.S. patent application Ser. No. 14/815,971, entitled “SECURITY THREATDETECTION USING DOMAIN NAME ACCESSES”, filed on 1 Aug. 2015, and U.S.patent application Ser. No. 14/815,972, entitled “SECURITY THREATDETECTION USING DOMAIN NAME REGISTRATIONS”, filed on 1 Aug. 2015, eachof which is hereby incorporated by reference in its entirety for allpurposes. Security-related information can also include malwareinfection data and system configuration information, as well as accesscontrol information, such as login/logout information and access failurenotifications. The security-related information can originate fromvarious sources within a data center, such as hosts, virtual machines,storage devices, and sensors. The security-related information can alsooriginate from various sources in a network, such as routers, switches,email servers, proxy servers, gateways, firewalls, andintrusion-detection systems.

During operation, the SPLUNK® APP FOR ENTERPRISE SECURITY facilitatesdetecting “notable events” that are likely to indicate a securitythreat. These notable events can be detected in a number of ways: (1) auser can notice a correlation in the data and can manually identify acorresponding group of one or more events as “notable;” or (2) a usercan define a “correlation search” specifying criteria for a notableevent, and every time one or more events satisfy the criteria, theapplication can indicate that the one or more events are notable. A usercan alternatively select a pre-defined correlation search provided bythe application. Note that correlation searches can be run continuouslyor at regular intervals (e.g., every hour) to search for notable events.Upon detection, notable events can be stored in a dedicated “notableevents index,” which can be subsequently accessed to generate variousvisualizations containing security-related information. Also, alerts canbe generated to notify system operators when important notable eventsare discovered.

The SPLUNK® APP FOR ENTERPRISE SECURITY provides various visualizationsto aid in discovering security threats, such as a “key indicators view”that enables a user to view security metrics, such as counts ofdifferent types of notable events. For example, FIG. 9A illustrates anexample key indicators view 900 that comprises a dashboard, which candisplay a value 901, for various security-related metrics, such asmalware infections 902. It can also display a change in a metric value903, which indicates that the number of malware infections increased by63 during the preceding interval. Key indicators view 900 additionallydisplays a histogram panel 904 that displays a histogram of notableevents organized by urgency values, and a histogram of notable eventsorganized by time intervals. This key indicators view is described infurther detail in pending U.S. patent application Ser. No. 13/956,338,entitled “KEY INDICATORS VIEW”, filed on 31 Jul. 2013, and which ishereby incorporated by reference in its entirety for all purposes.

These visualizations can also include an “incident review dashboard”that enables a user to view and act on “notable events.” These notableevents can include: (1) a single event of high importance, such as anyactivity from a known web attacker; or (2) multiple events thatcollectively warrant review, such as a large number of authenticationfailures on a host followed by a successful authentication. For example,FIG. 9B illustrates an example incident review dashboard 910 thatincludes a set of incident attribute fields 911 that, for example,enables a user to specify a time range field 912 for the displayedevents. It also includes a timeline 913 that graphically illustrates thenumber of incidents that occurred in time intervals over the selectedtime range. It additionally displays an events list 914 that enables auser to view a list of all of the notable events that match the criteriain the incident attributes fields 911. To facilitate identifyingpatterns among the notable events, each notable event can be associatedwith an urgency value (e.g., low, medium, high, critical), which isindicated in the incident review dashboard. The urgency value for adetected event can be determined based on the severity of the event andthe priority of the system component associated with the event.

2.12. Data Center Monitoring

As mentioned above, the SPLUNK® ENTERPRISE platform provides variousfeatures that simplify the task of creating various applications. Onesuch application is SPLUNK® APP FOR VMWARE® that provides operationalvisibility into granular performance metrics, logs, tasks and events,and topology from hosts, virtual machines and virtual centers. Itempowers administrators with an accurate real-time picture of the healthof the environment, proactively identifying performance and capacitybottlenecks.

Conventional data center-monitoring systems lack the infrastructure toeffectively store and analyze large volumes of machine-generated data,such as performance information and log data obtained from the datacenter. In conventional data center-monitoring systems,machine-generated data is typically pre-processed prior to being stored,for example, by extracting pre-specified data items and storing them ina database to facilitate subsequent retrieval and analysis at searchtime. However, the rest of the data is not saved and discarded duringpre-processing.

In contrast, the SPLUNK® APP FOR VMWARE® stores large volumes ofminimally processed machine data, such as performance information andlog data, at ingestion time for later retrieval and analysis at searchtime when a live performance issue is being investigated. In addition todata obtained from various log files, this performance-relatedinformation can include values for performance metrics obtained throughan application programming interface (API) provided as part of thevSphere Hypervisor™ system distributed by VMware, Inc. of Palo Alto,California. For example, these performance metrics can include: (1)CPU-related performance metrics; (2) disk-related performance metrics;(3) memory-related performance metrics; (4) network-related performancemetrics; (5) energy-usage statistics; (6) data-traffic-relatedperformance metrics; (7) overall system availability performancemetrics; (8) cluster-related performance metrics; and (9) virtualmachine performance statistics. Such performance metrics are describedin U.S. patent application Ser. No. 14/167,316, entitled “CORRELATIONFOR USER-SELECTED TIME RANGES OF VALUES FOR PERFORMANCE METRICS OFCOMPONENTS IN AN INFORMATION-TECHNOLOGY ENVIRONMENT WITH LOG DATA FROMTHAT INFORMATION-TECHNOLOGY ENVIRONMENT”, filed on 29 Jan. 2014, andwhich is hereby incorporated by reference in its entirety for allpurposes.

To facilitate retrieving information of interest from performance dataand log files, the SPLUNK® APP FOR VMWARE® provides pre-specifiedschemas for extracting relevant values from different types ofperformance-related event data, and also enables a user to define suchschemas.

The SPLUNK® APP FOR VMWARE® additionally provides various visualizationsto facilitate detecting and diagnosing the root cause of performanceproblems. For example, one such visualization is a “proactive monitoringtree” that enables a user to easily view and understand relationshipsamong various factors that affect the performance of a hierarchicallystructured computing system. This proactive monitoring tree enables auser to easily navigate the hierarchy by selectively expanding nodesrepresenting various entities (e.g., virtual centers or computingclusters) to view performance information for lower-level nodesassociated with lower-level entities (e.g., virtual machines or hostsystems). Example node-expansion operations are illustrated in FIG. 9C,wherein nodes 933 and 934 are selectively expanded. Note that nodes931-939 can be displayed using different patterns or colors to representdifferent performance states, such as a critical state, a warning state,a normal state or an unknown/offline state. The ease of navigationprovided by selective expansion in combination with the associatedperformance-state information enables a user to quickly diagnose theroot cause of a performance problem. The proactive monitoring tree isdescribed in further detail in U.S. patent application Ser. No.14/253,490, entitled “PROACTIVE MONITORING TREE WITH SEVERITY STATESORTING”, filed on 15 Apr. 2014, and U.S. patent application Ser. No.14/812,948, also entitled “PROACTIVE MONITORING TREE WITH SEVERITY STATESORTING”, filed on 29 Jul. 2015, each of which is hereby incorporated byreference in its entirety for all purposes.

The SPLUNK® APP FOR VMWARE® also provides a user interface that enablesa user to select a specific time range and then view heterogeneous datacomprising events, log data, and associated performance metrics for theselected time range. For example, the screen illustrated in FIG. 9Ddisplays a listing of recent “tasks and events” and a listing of recent“log entries” for a selected time range above a performance-metric graphfor “average CPU core utilization” for the selected time range. Notethat a user is able to operate pull-down menus 942 to selectivelydisplay different performance metric graphs for the selected time range.This enables the user to correlate trends in the performance-metricgraph with corresponding event and log data to quickly determine theroot cause of a performance problem. This user interface is described inmore detail in U.S. patent application Ser. No. 14/167,316, entitled“CORRELATION FOR USER-SELECTED TIME RANGES OF VALUES FOR PERFORMANCEMETRICS OF COMPONENTS IN AN INFORMATION-TECHNOLOGY ENVIRONMENT WITH LOGDATA FROM THAT INFORMATION-TECHNOLOGY ENVIRONMENT”, filed on 29 Jan.2014, and which is hereby incorporated by reference in its entirety forall purposes.

2.13. Cloud-Based System Overview

The example data intake and query system 108 described in reference toFIG. 2 comprises several system components, including one or moreforwarders, indexers, and search heads. In some environments, a user ofa data intake and query system 108 may install and configure, oncomputing devices owned and operated by the user, one or more softwareapplications that implement some or all of these system components. Forexample, a user may install a software application on server computersowned by the user and configure each server to operate as one or more ofa forwarder, an indexer, a search head, etc. This arrangement generallyis referred to as an “on-premises” solution. That is, the system 108 isinstalled and operates on computing devices directly controlled by theuser of the system. Some users might prefer an on-premises solutionbecause it provides a greater level of control over the configuration ofcertain aspects of the system (e.g., security, privacy, standards,controls, etc.). However, other users might instead prefer anarrangement in which the user is not directly responsible for providingand managing the computing devices upon which various components ofsystem 108 operate.

In one embodiment, to provide an alternative to an entirely on-premisesenvironment for system 108, one or more of the components of a dataintake and query system instead can be provided as a cloud-basedservice. In this context, a cloud-based service refers to a servicehosted by one more computing resources that are accessible to end usersover a network, for example, by using a web browser or other applicationon a client device to interface with the remote computing resources. Forexample, a service provider may provide a cloud-based data intake andquery system by managing computing resources configured to implementvarious aspects of the system (e.g., forwarders, indexers, search heads,etc.) and by providing access to the system to end users via a network.Typically, a user may pay a subscription or other fee to use such aservice. Each subscribing user of the cloud-based service may beprovided with an account that enables the user to configure a customizedcloud-based system based on the user's preferences.

FIG. 10 illustrates a block diagram of an example cloud-based dataintake and query system. Similar to the system of FIG. 2 , the networkedcomputer system 1000 includes input data sources 202 and forwarders 204.These input data sources and forwarders may be in a subscriber's privatecomputing environment. Alternatively, they might be directly managed bythe service provider as part of the cloud service. In the example system1000, one or more forwarders 204 and client devices 1002 are coupled toa cloud-based data intake and query system 1006 via one or more networks1004. Network 1004 broadly represents one or more LANs, WANs, cellularnetworks, intranetworks, internetworks, etc., using any of wired,wireless, terrestrial microwave, satellite links, etc., and may includethe public Internet, and is used by client devices 1002 and forwarders204 to access the system 1006. Similar to the system of 108, each of theforwarders 204 may be configured to receive data from an input sourceand to forward the data to other components of the system 1006 forfurther processing.

In an embodiment, a cloud-based data intake and query system 1006 maycomprise a plurality of system instances 1008. In general, each systeminstance 1008 may include one or more computing resources managed by aprovider of the cloud-based system 1006 made available to a particularsubscriber. The computing resources comprising a system instance 1008may, for example, include one or more servers or other devicesconfigured to implement one or more forwarders, indexers, search heads,and other components of a data intake and query system, similar tosystem 108. As indicated above, a subscriber may use a web browser orother application of a client device 1002 to access a web portal orother interface that enables the subscriber to configure an instance1008.

Providing a data intake and query system as described in reference tosystem 108 as a cloud-based service presents a number of challenges.Each of the components of a system 108 (e.g., forwarders, indexers andsearch heads) may at times refer to various configuration files storedlocally at each component. These configuration files typically mayinvolve some level of user configuration to accommodate particular typesof data a user desires to analyze and to account for other userpreferences. However, in a cloud-based service context, users typicallymay not have direct access to the underlying computing resourcesimplementing the various system components (e.g., the computingresources comprising each system instance 1008), and may instead desireto make such configurations indirectly (e.g., using web-basedinterfaces). Thus, the techniques and systems described herein areapplicable to both on-premises and cloud-based service contexts, or somecombination thereof (e.g., a hybrid system where both an on-premisesenvironment such as SPLUNK® ENTERPRISE and a cloud-based environmentsuch as SPLUNK CLOUD™ are centrally visible).

2.14. Searching Externally Archived Data

FIG. 11 shows a block diagram of an example of a data intake and querysystem 108 that provides transparent search facilities for data systemsthat are external to the data intake and query system. Such facilitiesare available in the HUNK® system provided by Splunk Inc. of SanFrancisco, California. HUNK® represents an analytics platform thatenables business and IT teams to rapidly explore, analyze, and visualizedata in Hadoop and NoSQL data stores.

The search head 210 of the data intake and query system receives searchrequests from one or more client devices 1104 over network connections1120. As discussed above, the data intake and query system 108 mayreside in an enterprise location, in the cloud, etc. FIG. 11 illustratesthat multiple client devices 1104 a, 1104 b, . . . , 1104 n maycommunicate with the data intake and query system 108. The clientdevices 1104 may communicate with the data intake and query system usinga variety of connections. For example, one client device in FIG. 11 isillustrated as communicating over an Internet (Web) protocol, anotherclient device is illustrated as communicating via a command lineinterface, and another client device is illustrated as communicating viaa system developer kit (SDK).

The search head 210 analyzes the received search request to identifyrequest parameters. If a search request received from one of the clientdevices 1104 references an index maintained by the data intake and querysystem, then the search head 210 connects to one or more indexers 206 ofthe data intake and query system for the index referenced in the requestparameters. That is, if the request parameters of the search requestreference an index, then the search head accesses the data in the indexvia the indexer. The data intake and query system 108 may include one ormore indexers 206, depending on system access resources andrequirements. As described further below, the indexers 206 retrieve datafrom their respective local data stores 208 as specified in the searchrequest. The indexers and their respective data stores can comprise oneor more storage devices and typically reside on the same system, thoughthey may be connected via a local network connection.

If the request parameters of the received search request reference anexternal data collection, which is not accessible to the indexers 206 orunder the management of the data intake and query system, then thesearch head 210 can access the external data collection through anExternal Result Provider (ERP) process 1110. An external data collectionmay be referred to as a “virtual index” (plural, “virtual indices”). AnERP process provides an interface through which the search head 210 mayaccess virtual indices.

Thus, a search reference to an index of the system relates to a locallystored and managed data collection. In contrast, a search reference to avirtual index relates to an externally stored and managed datacollection, which the search head may access through one or more ERPprocesses 1110, 1112. FIG. 11 shows two ERP processes 1110, 1112 thatconnect to respective remote (external) virtual indices, which areindicated as a Hadoop or another system 1114 (e.g., Amazon S3, AmazonElastic MapReduce (EMR), other Hadoop Compatible File Systems (HCFS),etc.) and a relational database management system (RDBMS) 1116. Othervirtual indices may include other file organizations and protocols, suchas Structured Query Language (SQL) and the like. The ellipses betweenthe ERP processes 1110, 1112 indicate optional additional ERP processesof the data intake and query system 108. An ERP process may be acomputer process that is initiated or spawned by the search head 210 andis executed by the search data intake and query system 108.Alternatively or additionally, an ERP process may be a process spawnedby the search head 210 on the same or different host system as thesearch head 210 resides.

The search head 210 may spawn a single ERP process in response tomultiple virtual indices referenced in a search request, or the searchhead may spawn different ERP processes for different virtual indices.Generally, virtual indices that share common data configurations orprotocols may share ERP processes. For example, all search queryreferences to a Hadoop file system may be processed by the same ERPprocess, if the ERP process is suitably configured. Likewise, all searchquery references to an SQL database may be processed by the same ERPprocess. In addition, the search head may provide a common ERP processfor common external data source types (e.g., a common vendor may utilizea common ERP process, even if the vendor includes different data storagesystem types, such as Hadoop and SQL). Common indexing schemes also maybe handled by common ERP processes, such as flat text files or Weblogfiles.

The search head 210 determines the number of ERP processes to beinitiated via the use of configuration parameters that are included in asearch request message. Generally, there is a one-to-many relationshipbetween an external results provider “family” and ERP processes. Thereis also a one-to-many relationship between an ERP process andcorresponding virtual indices that are referred to in a search request.For example, using RDBMS, assume two independent instances of such asystem by one vendor, such as one RDBMS for production and another RDBMSused for development. In such a situation, it is likely preferable (butoptional) to use two ERP processes to maintain the independent operationas between production and development data. Both of the ERPs, however,will belong to the same family, because the two RDBMS system types arefrom the same vendor.

The ERP processes 1110, 1112 receive a search request from the searchhead 210. The search head may optimize the received search request forexecution at the respective external virtual index. Alternatively, theERP process may receive a search request as a result of analysisperformed by the search head or by a different system process. The ERPprocesses 1110, 1112 can communicate with the search head 210 viaconventional input/output routines (e.g., standard in/standard out,etc.). In this way, the ERP process receives the search request from aclient device such that the search request may be efficiently executedat the corresponding external virtual index.

The ERP processes 1110, 1112 may be implemented as a process of the dataintake and query system. Each ERP process may be provided by the dataintake and query system, or may be provided by process or applicationproviders who are independent of the data intake and query system. Eachrespective ERP process may include an interface application installed ata computer of the external result provider that ensures propercommunication between the search support system and the external resultprovider. The ERP processes 1110, 1112 generate appropriate searchrequests in the protocol and syntax of the respective virtual indices1114, 1116, each of which corresponds to the search request received bythe search head 210. Upon receiving search results from theircorresponding virtual indices, the respective ERP process passes theresult to the search head 210, which may return or display the resultsor a processed set of results based on the returned results to therespective client device.

Client devices 1104 may communicate with the data intake and querysystem 108 through a network interface 1120, e.g., one or more LANs,WANs, cellular networks, intranetworks, and/or internetworks using anyof wired, wireless, terrestrial microwave, satellite links, etc., andmay include the public Internet.

The analytics platform utilizing the External Result Provider processdescribed in more detail in U.S. Pat. No. 8,738,629, entitled “EXTERNALRESULT PROVIDED PROCESS FOR RETRIEVING DATA STORED USING A DIFFERENTCONFIGURATION OR PROTOCOL”, issued on 27 May 2014, U.S. Pat. No.8,738,587, entitled “PROCESSING A SYSTEM SEARCH REQUEST BY RETRIEVINGRESULTS FROM BOTH A NATIVE INDEX AND A VIRTUAL INDEX”, issued on 25 Jul.2013, U.S. patent application Ser. No. 14/266,832, entitled “PROCESSINGA SYSTEM SEARCH REQUEST ACROSS DISPARATE DATA COLLECTION SYSTEMS”, filedon 1 May 2014, and U.S. patent application Ser. No. 14/449,144, entitled“PROCESSING A SYSTEM SEARCH REQUEST INCLUDING EXTERNAL DATA SOURCES”,filed on 31 Jul. 2014, each of which is hereby incorporated by referencein its entirety for all purposes.

2.14.1. ERP Process Features

The ERP processes described above may include two operation modes: astreaming mode and a reporting mode. The ERP processes can operate instreaming mode only, in reporting mode only, or in both modessimultaneously. Operating in both modes simultaneously is referred to asmixed mode operation. In a mixed mode operation, the ERP at some pointcan stop providing the search head with streaming results and onlyprovide reporting results thereafter, or the search head at some pointmay start ignoring streaming results it has been using and only usereporting results thereafter.

The streaming mode returns search results in real time, with minimalprocessing, in response to the search request. The reporting modeprovides results of a search request with processing of the searchresults prior to providing them to the requesting search head, which inturn provides results to the requesting client device. ERP operationwith such multiple modes provides greater performance flexibility withregard to report time, search latency, and resource utilization.

In a mixed mode operation, both streaming mode and reporting mode areoperating simultaneously. The streaming mode results (e.g., the raw dataobtained from the external data source) are provided to the search head,which can then process the results data (e.g., break the raw data intoevents, timestamp it, filter it, etc.) and integrate the results datawith the results data from other external data sources, and/or from datastores of the search head. The search head performs such processing andcan immediately start returning interim (streaming mode) results to theuser at the requesting client device; simultaneously, the search head iswaiting for the ERP process to process the data it is retrieving fromthe external data source as a result of the concurrently executingreporting mode.

In some instances, the ERP process initially operates in a mixed mode,such that the streaming mode operates to enable the ERP quickly toreturn interim results (e.g., some of the raw or unprocessed datanecessary to respond to a search request) to the search head, enablingthe search head to process the interim results and begin providing tothe client or search requester interim results that are responsive tothe query. Meanwhile, in this mixed mode, the ERP also operatesconcurrently in reporting mode, processing portions of raw data in amanner responsive to the search query. Upon determining that it hasresults from the reporting mode available to return to the search head,the ERP may halt processing in the mixed mode at that time (or somelater time) by stopping the return of data in streaming mode to thesearch head and switching to reporting mode only. The ERP at this pointstarts sending interim results in reporting mode to the search head,which in turn may then present this processed data responsive to thesearch request to the client or search requester. Typically the searchhead switches from using results from the ERP's streaming mode ofoperation to results from the ERP's reporting mode of operation when thehigher bandwidth results from the reporting mode outstrip the amount ofdata processed by the search head in the]streaming mode of ERPoperation.

A reporting mode may have a higher bandwidth because the ERP does nothave to spend time transferring data to the search head for processingall the raw data. In addition, the ERP may optionally direct anotherprocessor to do the processing.

The streaming mode of operation does not need to be stopped to gain thehigher bandwidth benefits of a reporting mode; the search head couldsimply stop using the streaming mode results—and start using thereporting mode results—when the bandwidth of the reporting mode hascaught up with or exceeded the amount of bandwidth provided by thestreaming mode. Thus, a variety of triggers and ways to accomplish asearch head's switch from using streaming mode results to usingreporting mode results may be appreciated by one skilled in the art.

The reporting mode can involve the ERP process (or an external system)performing event breaking, time stamping, filtering of events to matchthe search query request, and calculating statistics on the results. Theuser can request particular types of data, such as if the search queryitself involves types of events, or the search request may ask forstatistics on data, such as on events that meet the search request. Ineither case, the search head understands the query language used in thereceived query request, which may be a proprietary language. Oneexemplary query language is Splunk Processing Language (SPL) developedby the assignee of the application, Splunk Inc. The search headtypically understands how to use that language to obtain data from theindexers, which store data in a format used by the SPLUNK® Enterprisesystem.

The ERP processes support the search head, as the search head is notordinarily configured to understand the format in which data is storedin external data sources such as Hadoop or SQL data systems. Rather, theERP process performs that translation from the query submitted in thesearch support system's native format (e.g., SPL if SPLUNK® ENTERPRISEis used as the search support system) to a search query request formatthat will be accepted by the corresponding external data system. Theexternal data system typically stores data in a different format fromthat of the search support system's native index format, and it utilizesa different query language (e.g., SQL or MapReduce, rather than SPL orthe like).

As noted, the ERP process can operate in the streaming mode alone. Afterthe ERP process has performed the translation of the query request andreceived raw results from the streaming mode, the search head canintegrate the returned data with any data obtained from local datasources (e.g., native to the search support system), other external datasources, and other ERP processes (if such operations were required tosatisfy the terms of the search query). An advantage of mixed modeoperation is that, in addition to streaming mode, the ERP process isalso executing concurrently in reporting mode. Thus, the ERP process(rather than the search head) is processing query results (e.g.,performing event breaking, timestamping, filtering, possibly calculatingstatistics if required to be responsive to the search query request,etc.). It should be apparent to those skilled in the art that additionaltime is needed for the ERP process to perform the processing in such aconfiguration. Therefore, the streaming mode will allow the search headto start returning interim results to the user at the client devicebefore the ERP process can complete sufficient processing to startreturning any search results. The switchover between streaming andreporting mode happens when the ERP process determines that theswitchover is appropriate, such as when the ERP process determines itcan begin returning meaningful results from its reporting mode.

The operation described above illustrates the source of operationallatency: streaming mode has low latency (immediate results) and usuallyhas relatively low bandwidth (fewer results can be returned per unit oftime). In contrast, the concurrently running reporting mode hasrelatively high latency (it has to perform a lot more processing beforereturning any results) and usually has relatively high bandwidth (moreresults can be processed per unit of time). For example, when the ERPprocess does begin returning report results, it returns more processedresults than in the streaming mode, because, e.g., statistics only needto be calculated to be responsive to the search request. That is, theERP process doesn't have to take time to first return raw data to thesearch head. As noted, the ERP process could be configured to operate instreaming mode alone and return just the raw data for the search head toprocess in a way that is responsive to the search request.Alternatively, the ERP process can be configured to operate in thereporting mode only. Also, the ERP process can be configured to operatein streaming mode and reporting mode concurrently, as described, withthe ERP process stopping the transmission of streaming results to thesearch head when the concurrently running reporting mode has caught upand started providing results. The reporting mode does not require theprocessing of all raw data that is responsive to the search queryrequest before the ERP process starts returning results; rather, thereporting mode usually performs processing of chunks of events andreturns the processing results to the search head for each chunk.

For example, an ERP process can be configured to merely return thecontents of a search result file verbatim, with little or no processingof results. That way, the search head performs all processing (such asparsing byte streams into events, filtering, etc.). The ERP process canbe configured to perform additional intelligence, such as analyzing thesearch request and handling all the computation that a native searchindexer process would otherwise perform. In this way, the configured ERPprocess provides greater flexibility in features while operatingaccording to desired preferences, such as response latency and resourcerequirements.

2.14. IT Service Monitoring

As previously mentioned, the SPLUNK® ENTERPRISE platform providesvarious schemas, dashboards and visualizations that make it easy fordevelopers to create applications to provide additional capabilities.One such application is SPLUNK® IT SERVICE INTELLIGENCE™, which performsmonitoring and alerting operations. It also includes analytics to helpan analyst diagnose the root cause of performance problems based onlarge volumes of data stored by the SPLUNK® ENTERPRISE system ascorrelated to the various services an IT organization provides (aservice-centric view). This differs significantly from conventional ITmonitoring systems that lack the infrastructure to effectively store andanalyze large volumes of service-related event data. Traditional servicemonitoring systems typically use fixed schemas to extract data frompre-defined fields at data ingestion time, wherein the extracted data istypically stored in a relational database. This data extraction processand associated reduction in data content that occurs at data ingestiontime inevitably hampers future investigations, when all of the originaldata may be needed to determine the root cause of or contributingfactors to a service issue.

In contrast, a SPLUNK® IT SERVICE INTELLIGENCE™ system stores largevolumes of minimally-processed service-related data at ingestion timefor later retrieval and analysis at search time, to perform regularmonitoring, or to investigate a service issue. To facilitate this dataretrieval process, SPLUNK® IT SERVICE INTELLIGENCE™ enables a user todefine an IT operations infrastructure from the perspective of theservices it provides. In this service-centric approach, a service suchas corporate e-mail may be defined in terms of the entities employed toprovide the service, such as host machines and network devices. Eachentity is defined to include information for identifying all of theevent data that pertains to the entity, whether produced by the entityitself or by another machine, and considering the many various ways theentity may be identified in raw machine data (such as by a URL, an IPaddress, or machine name). The service and entity definitions canorganize event data around a service so that all of the event datapertaining to that service can be easily identified. This capabilityprovides a foundation for the implementation of Key PerformanceIndicators.

One or more Key Performance Indicators (KPI's) are defined for a servicewithin the SPLUNK® IT SERVICE INTELLIGENCE™ application. Each KPImeasures an aspect of service performance at a point in time or over aperiod of time (aspect KPI's). Each KPI is defined by a search querythat derives a KPI value from the machine data of events associated withthe entities that provide the service. Information in the entitydefinitions may be used to identify the appropriate events at the time aKPI is defined or whenever a KPI value is being determined. The KPIvalues derived over time may be stored to build a valuable repository ofcurrent and historical performance information for the service, and therepository, itself, may be subject to search query processing. AggregateKPIs may be defined to provide a measure of service performancecalculated from a set of service aspect KPI values; this aggregate mayeven be taken across defined timeframes and/or across multiple services.A particular service may have an aggregate KPI derived fromsubstantially all of the aspect KPI's of the service to indicate anoverall health score for the service.

SPLUNK® IT SERVICE INTELLIGENCE™ facilitates the production ofmeaningful aggregate KPI's through a system of KPI thresholds and statevalues. Different KPI definitions may produce values in differentranges, and so the same value may mean something very different from oneKPI definition to another. To address this, SPLUNK® IT SERVICEINTELLIGENCE™ implements a translation of individual KPI values to acommon domain of “state” values. For example, a KPI range of values maybe 1-100, or 50-275, while values in the state domain may be ‘critical,’‘warning,’ ‘normal,’ and ‘informational’. Thresholds associated with aparticular KPI definition determine ranges of values for that KPI thatcorrespond to the various state values. In one case, KPI values 95-100may be set to correspond to ‘critical’ in the state domain. KPI valuesfrom disparate KPI's can be processed uniformly once they are translatedinto the common state values using the thresholds. For example, “normal80% of the time” can be applied across various KPI's. To providemeaningful aggregate KPI's, a weighting value can be assigned to eachKPI so that its influence on the calculated aggregate KPI value isincreased or decreased relative to the other KPI's.

One service in an IT environment often impacts, or is impacted by,another service. SPLUNK® IT SERVICE INTELLIGENCE™ can reflect thesedependencies. For example, a dependency relationship between a corporatee-mail service and a centralized authentication service can be reflectedby recording an association between their respective servicedefinitions. The recorded associations establish a service dependencytopology that informs the data or selection options presented in agraphical user interface (GUI), for example. (The service dependencytopology is like a “map” showing how services are connected based ontheir dependencies.) The service topology may itself be depicted in aGUI and may be interactive to allow navigation among related services.

Entity definitions in SPLUNK® IT SERVICE INTELLIGENCE™ can includeinformational fields that can serve as metadata, implied data fields, orattributed data fields for the events identified by other aspects of theentity definition. Entity definitions in SPLUNK® IT SERVICEINTELLIGENCE™ can also be created and updated by an import of tabulardata (as represented in a comma-separate values (CSV) file, anotherdelimited file, or a search query result set). The import may beGUI-mediated or processed using import parameters from a GUI-basedimport definition process. Entity definitions in SPLUNK® IT SERVICEINTELLIGENCE™ can also be associated with a service by means of aservice definition rule. Processing the rule results in the matchingentity definitions being associated with the service definition. Therule can be processed at creation time, and thereafter on a scheduled oron-demand basis. This allows dynamic, rule-based updates to the servicedefinition.

During operation, SPLUNK® IT SERVICE INTELLIGENCE™ can recognizeso-called “notable events” that may indicate a service performanceproblem or other situation of interest. These notable events can berecognized by a “correlation search” specifying trigger criteria for anotable event: every time KPI values satisfy the criteria, theapplication indicates a notable event. A severity level for the notableevent may also be specified. Furthermore, when trigger criteria aresatisfied, the correlation search may additionally or alternativelycause a service ticket to be created in an IT service management (ITSM)system, such as a systems available from ServiceNow, Inc., of SantaClara, California.

SPLUNK® IT SERVICE INTELLIGENCE™ provides various visualizations builton its service-centric organization of event data and the KPI valuesgenerated and collected. Visualizations can be particularly useful formonitoring or investigating service performance. SPLUNK® IT SERVICEINTELLIGENCE™ provides a service monitoring interface suitable as thehome page for ongoing IT service monitoring. The interface isappropriate for settings such as desktop use or for a wall-mounteddisplay in a network operations center (NOC). The interface mayprominently display a services health section with tiles for theaggregate KPI's indicating overall health for defined services and ageneral KPI section with tiles for KPI's related to individual serviceaspects. These tiles may display KPI information in a variety of ways,such as by being colored and ordered according to factors like the KPIstate value. They also can be interactive and navigate to visualizationsof more detailed KPI information.

SPLUNK® IT SERVICE INTELLIGENCE™ provides a service-monitoring dashboardvisualization based on a user-defined template. The template can includeuser-selectable widgets of varying types and styles to display KPIinformation. The content and the appearance of widgets can responddynamically to changing KPI information. The KPI widgets can appear inconjunction with a background image, user drawing objects, or othervisual elements, that depict the IT operations environment, for example.The KPI widgets or other GUI elements can be interactive so as toprovide navigation to visualizations of more detailed KPI information.

SPLUNK® IT SERVICE INTELLIGENCE™ provides a visualization showingdetailed time-series information for multiple KPI's in parallel graphlanes. The length of each lane can correspond to a uniform time range,while the width of each lane may be automatically adjusted to fit thedisplayed KPI data. Data within each lane may be displayed in a userselectable style, such as a line, area, or bar chart. During operation auser may select a position in the time range of the graph lanes toactivate lane inspection at that point in time. Lane inspection maydisplay an indicator for the selected time across the graph lanes anddisplay the KPI value associated with that point in time for each of thegraph lanes. The visualization may also provide navigation to aninterface for defining a correlation search, using information from thevisualization to pre-populate the definition.

SPLUNK® IT SERVICE INTELLIGENCE™ provides a visualization for incidentreview showing detailed information for notable events. The incidentreview visualization may also show summary information for the notableevents over a time frame, such as an indication of the number of notableevents at each of a number of severity levels. The severity leveldisplay may be presented as a rainbow chart with the warmest colorassociated with the highest severity classification. The incident reviewvisualization may also show summary information for the notable eventsover a time frame, such as the number of notable events occurring withinsegments of the time frame. The incident review visualization maydisplay a list of notable events within the time frame ordered by anynumber of factors, such as time or severity. The selection of aparticular notable event from the list may display detailed informationabout that notable event, including an identification of the correlationsearch that generated the notable event.

SPLUNK® IT SERVICE INTELLIGENCE™ provides pre-specified schemas forextracting relevant values from the different types of service-relatedevent data. It also enables a user to define such schemas.

3.0. FUNCTIONAL OVERVIEW

Approaches, techniques, and mechanisms are disclosed for generatingvisualizations which graphically depict network activity occurringbetween pairs of networked devices. In an embodiment, the visualizationsare based on data indicating the depicted network activity, where thenetwork activity can involve devices having any network addresses withinan entire network address space (e.g., any address within the InternetProtocol version v4 (IPv4) or IPv6 network address space), or within asubset of an entire network address space. The ability to visualizehigh-level information related to network activity occurring across anentire network address space enables network analysts and other users toreadily analyze characteristics of computer networks which otherwisemight not be evident or difficult to obtain using other types ofvisualizations.

According to embodiments described herein, a graphical interfacedisplaying network activity visualizations further includes interfaceelements enabling various types of interactions with suchvisualizations, including the ability to zoom in and out of selectedsegments of a visualization, to view detailed information related toselected elements of a visualization, to view animated displays ofnetwork activity data, and to temporally scan visualizations in the timedomain.

As an example, consider a network analyst tasked with examining acorporate network for the first time. A typical corporate network mightcomprise many thousands of networked devices, including desktopcomputers, servers, mobile devices, routers, and other types of devicesdistributed throughout the corporate network. In this example, furtherassume that the corporate network includes components (e.g., networktaps, logging software, etc.) that monitor network traffic traversingthe corporate network and which further generate network activity databased on the monitored network traffic (e.g., log data, timestampedevent data, event notification data, etc.). The data generated toreflect network activity involving devices of the corporate networkmight contain a wealth of information, but such data can be cumbersometo analyze without the assistance of a data analysis system, such as adata intake and query system 108 described in Section 2.0.

In the example above, to provide groundwork for a network analyst'sfurther examination of the corporate network, the analyst might desirethe ability to see a high-level view of computer network activityoccurring across some or all of the devices in the corporate network.This high-level view might include, for example, information aboutinternal and external devices with which the devices on the corporatenetwork are communicating, how much data was transferred to and fromparticular devices, and what times of day particular pairs of devicesgenerated more or less network traffic. However, assuming an IPv4 orother similar network infrastructure, each of the devices on thecorporate network can conceivably have almost any one of the billions ofnetwork addresses in the range of 0.0.0.0 to 255.255.255.255. Similarly,each destination device to which a source device communicates can have anetwork address falling almost anywhere in the same range. Thus, theability to display any of the vast number of possible sourcedevice-to-destination device network activity permutations in a singlevisualization presents a number of challenges.

The network activity visualizations described herein enable the displayof network activity spanning an entire network address space or a subsetthereof in a single visualization, among other benefits. In oneembodiment, a network activity visualization includes a graphicalrepresentation of instances of network activity, where each instance ofnetwork activity corresponds to network activity which occurred at somepoint in time between two networked computing devices of a plurality ofnetworked computing devices. The graphical representation includes aplurality of data points, where each data point represents a range ofsource network addresses and a range of destination network addresses.Each data point further represents at least one instance of networkactivity, the at least one instance of network activity associated witha source network address within the range of source network addresses,and associated with a destination network address within the range ofdestination network addresses represented by the data point. Bygenerating and displaying a visualization of instances of networkactivity contained in network activity data, where the instances ofnetwork activity are grouped based on ranges of source network addressesand destination network addresses, network activity data related to alarge number of networked devices can be efficiently and usefullydisplayed in a single visualization.

In one embodiment, a data intake and query system 108 includescomponents for generating and causing display of network activityvisualizations as described herein. In other embodiments, networkactivity visualizations may be generated by a component of another typeof data analysis application, by a standalone application, or by anyother type of application. In other aspects, embodiments of theinvention encompass a computer apparatus and a computer-readable mediumconfigured to carry out the foregoing.

3.1. Network Activity Visualization Overview

In an embodiment, the network activity visualizations described hereinare generated based on data indicating network activity involving amonitored set of networked computing devices, network links, or both. Asused herein, network activity data can broadly include any type of datawhich indicates network activity involving a set of networked devicesincluding, but not limited to, log data, timestamped event data,real-time network activity data, or combinations thereof.

In an embodiment, some or all of the network activity data upon which anetwork activity visualization is based can be generated by devices towhich the network activity data relates (e.g., by logging componentsrunning on the devices), by separate network taps or other componentswhich monitor network traffic traversing a network, or by any other typeof network activity data-generating component. In some embodiments, someor all of the network activity data can be obtained from externalsources (e.g., log data obtained from an external source and relating toan external network). In some embodiments, network activity data caninclude real-time network traffic information detected by a networkmonitoring component, but which is not stored in a log or other dataformat.

In one embodiment, network activity data indicates instances of networkactivity, where each instance of network activity corresponds to networkactivity occurring between at least two networked devices: a sourcedevice and a destination device. In this context, an instance of networkactivity can broadly refer to any type of computer network-basedcommunication occurring between a pair of devices, includingcommunication to establish a network connection between the devices(e.g., establishment of a Transmission Control Protocol (TCP)connection, Hypertext Transfer Protocol (HTTP), Secure Shell (SSH), orany other type of network connection), an occurrence of a network dataflow between the devices, an occurrence of a network session involvingthe devices, or any other type of network activity.

In an embodiment, an instance of network activity includes dataidentifying a source device network address, a destination devicenetwork address, and at least one timestamp associated with the instanceof network activity (e.g., indicating a time at which a correspondingconnection was established, a time at which a network session wasinitiated or ended, etc.). An instance of network activity may includeother types of data, including information indicating an amount of datatransferred during the instance of network activity, a number of packetstransferred during the instance of network activity, a type of sourcedevice and destination device, a duration of the instance of networkactivity, and so forth. As one example, if the network activity dataincludes log data, an instance of network activity might correspond toone or more entries in a network activity log. As another example, aninstance of network activity might correspond to one or more timestampedevents stored at an indexer of a data intake and query system 108, wherethe timestamped events are derived from log data, monitored networktraffic, or any other data source.

In an embodiment, each of the devices associated with an instance ofnetwork activity broadly can be any type of networked computing deviceincluding, but not limited to, a client computer, a server, a mobiledevice, a network device, etc. For example, a source device associatedwith one instance of network activity might be a desktop computerconnected to a corporate network, and the destination device might be anexternal web server with which the desktop computer is communicating(e.g., as part of an HTTP session to retrieve one or more webpages fromthe external web server). As another example, a source device associatedwith another network event might be a mobile device, and the destinationdevice might be an email server on the same local network as the mobiledevice. As these examples illustrate, instances of network activity inaggregate may represent the collective activity of any number ofmonitored devices on a network, including information about internal andexternal devices with which those devices are communicating.

As indicated above, an instance of network activity contained within thenetwork activity data involves at least two devices: a source device anda destination device. In one embodiment, the identification of onedevice as the source device and a second device as the destinationdevice is included in the network activity data. For example, a networktap or other monitoring component generating the network activity datamight identify each of the devices associated with an instance ofnetwork activity as either the “source” or “destination” device when thenetwork activity data is generated (e.g., based on determining whichdevice initiated the network activity between the devices or othercriteria). In other embodiments, a data intake and query system 108 orother component can identify a “source” and “destination” device basedon analyzing network activity data subsequent to generation of thenetwork activity data.

FIG. 18 illustrates an example graphical interface displaying a networkactivity visualization generated based on network activity data. In FIG.18 , a network activity visualization 1802 is one of several dashboardpanels displayed on a dashboard interface 1800, where each dashboardpanel displays a different type of network analytics visualization. Inthe example of FIG. 18 , network activity visualization 1802 displaysinformation relating to network activity which has occurred betweenpairs of devices having any network address within the entire IPv4address space (i.e., devices associated with any network address between0.0.0.0 and 255.255.255.255 inclusive). The devices represented in thenetwork activity visualization 1802, for example, might include devicesof a network for which an analyst is tasked with monitoring and forwhich a data intake and query system 108 has collected network activitydata.

The network activity visualization 1802 includes a chart having a firstaxis 1804 (the x-axis), where each horizontal position on the first axis1804 corresponds to a range of source network addresses, and a secondaxis 1806 (the y-axis), where each vertical position on the second axis1806 corresponds to a range of destination network addresses. In theexample of FIG. 18 and in other examples elsewhere herein, networkactivity visualization 1802 is displayed using a bubble chart, whereeach data point in the chart is depicted as a circular “bubble”representing one or more instances of network activity from the networkactivity data; however, other types of charts can be used to visualizenetwork activity data according to the embodiments described herein.

In an embodiment, the visualization 1802 includes a plurality plotteddata points (e.g., including data points 1808) displayed at variouslocations on the chart relative to the first axis 1804 and second axis1806. In an embodiment, each data point displayed on the chartrepresents at least one instance of network activity from the networkactivity data upon which the visualization 1802 is based. Morespecifically, each data point plotted in the visualization 1802represents one or more instances of network activity associated with asource device and destination device having network addresses fallingwithin the ranges of network addresses represented by the location onthe chart. For example, one data point might represent instances ofnetwork activity associated with a source device having a networkaddress anywhere in the range 10.16.0.0-10.16.255.255, and furtherassociated with a destination device network address anywhere in therange 102.116.0.0-102.116.255.255. This example data point is displayedat a location relative to the first axis and second axis correspondingto the 10.16.*.* and 102.116.*.* network address ranges, respectively(where the “*” character represents any value between 0 and 255). Inthis manner, instances of network activity involving devices within asame range of source network addresses and destination network addressescan be displayed using a same data point, thereby enabling networkactivity associated with devices having network addresses distributedacross an entire network to be displayed more efficiently and usefullyon a single visualization.

As described in more detail elsewhere herein, various types of visualindicators can be used to display additional information about networkactivity data represented by a network activity visualization, such asvisualization 1802. As one example, a relative size, or area, of eachdata point displayed in a visualization can be used to indicate any of:a number of instances of network activity represented by a data point, anumber of packets transferred during the represented instances ofnetwork activity, a number of bytes transferred during the representedinstances of network activity, or any other value or metric associatedwith the represented instances of network activity. As shown in FIG. 18, for example, some of the data points plotted in the visualization 1802are displayed as circular bubbles larger or smaller in area relative tothe bubbles representing other data points (e.g., possibly indicatingthat more or fewer instances of network activity are associated withsome data points relative to others). Other example visual indicatorscan include displaying data points with an opacity level correspondingto an age of the represented instances of network activity (e.g., basedon timestamps associated with the represented instances of networkactivity), using color coding to identify data points associated withreserved network address ranges, animating the display of data points toshow network activity occurring over time, enabling users to temporallyscan the display of data points forward and back in time, and so forth.

3.2. Generating Visualizations Based on Network Activity Data

FIG. 19 is a flow diagram illustrating an example process for generatingand causing display of network activity visualizations involving deviceshaving network addresses distributed across an entire network addressspace or a subset thereof. The various elements of flow 1900 may beperformed in a variety of systems, including systems such as system 108described above. In an embodiment, each of the processes described inconnection with the functional blocks described below may be implementedusing one or more computer programs, other software elements, and/ordigital logic in any of a general-purpose computer or a special-purposecomputer, while performing data retrieval, transformation, and storageoperations that involve interacting with and transforming the physicalstate of memory of the computer.

At block 1902, a data intake and query system identifies networkactivity data indicating instances of network activity, and upon which anetwork activity visualization is to be based. A data intake and querysystem 108 might identify the data in response to a request to generateand display an interface including a network activity visualization, inresponse to executing a user query to locate particular network activitydata of interest, based on a scheduled or periodically executed query,or in response to any other action or condition.

Referring to FIG. 18 , for example, a user might request display of aweb-based interface or any other type of graphical interface including anetwork activity visualization 1802 and, in response to the request, thedata intake and query system 108 identifies the network activity dataused to generate the visualization. In an embodiment, the networkactivity data identified and used to generate a network activity may beselected by default (e.g., by identifying any network activity dataavailable to a data intake and query system 108), or identified based ona selection of particular network activity data to use (e.g., based on auser query, based on selection of the data from a list, or based on anyother selection criteria).

In an embodiment, identifying the network activity data can includeidentifying all available network activity, network activity dataassociated with a specified range of time, network activity dataassociated with a specified set of devices, network activity dataassociated with one or more specified types of instances of networkactivity, or network activity selected based on any other criteria orcombination thereof. For example, a user might request display of aninterface displaying a network activity visualization showing allnetwork activity occurring in the past week, only network activityassociated with mobile devices connected to a network, only networkactivity associated with HTTP and SSH traffic, etc.

At block 1904, the data intake and query system determines, based on theidentified network activity data, instances of network activity betweenthe networked devices. For example, the data intake and query system candetermine instances of network activity by analyzing the networkactivity to identify events corresponding to instances of networkactivity of interest. In an embodiment, each instance of networkactivity (e.g., a network connection, a network flow, a network session,etc.) can correspond to one or more separate events of the networkactivity data.

At block 1906, the data intake and query system causes display of agraphical representation used to display the network activity data. Asindicated above, a data intake and query system 108 may cause display ofa network activity visualization by generating data used to display thechart as part of a web-based interface, application interface, or anyother type of graphical interface. In one embodiment, a data intake andquery system 108 generates an HTML, document including the chart whichcan be displayed at any client device with access to the system 108. Inan embodiment, the chart may be generated as a static display (e.g., animage file), or as a dynamic and interactive element of an interface(e.g., a collection of image files, interface elements, and other logicused to display the chart).

In an embodiment, the graphical representation includes a plurality ofdata points on the visualization representing some or all of theinstances of network activity identified at block 1904. In anembodiment, each data point represents a range of source networkaddresses and a range of destination network addresses. Each data pointfurther represents at least one instance of network activity associatedwith a source network address within the range of source networkaddresses, and a destination network address within the range ofdestination network addresses.

In an embodiment, a size of the range of network addresses representedby each data point may be selected automatically, based on a userselection, or based any other condition. For example, a data intake andquery system 108 might use a default range corresponding to the last twooctets of an IP address (e.g., ranges corresponding to the form a.b.*.*,where the “*” character represents any value in the range 0-255.) Inthis example, one range of source network addresses corresponds toaddresses falling in the range 100.1.*.*, a next range of source networkaddresses in this example is 100.2.*.*, followed by 100.3.*.*, and soforth.

In an embodiment, a size of the network addresses ranges can bespecified and updated based on user input. For example, subsequent toviewing a visualization based on network address ranges corresponding tothe last two octets of an IP address, a user may provide inputrequesting to update the visualization display where the range for eachdata point spans only the last octet of an IP address (e.g., such thatone data point corresponds to addresses within the range 100.1.1.*, thenext range is 100.1.2.*, and so forth). In general, as the size of thenetwork address ranges is increased, the number of data points displayedin a network activity visualization decreases, and vice versa. However,because fewer data points are displayed as the range size increases,there is a corresponding loss of granularity with respect to theidentity of the devices represented by the visualization. In thismanner, a user can adjust the size of the network address ranges basedon the user's preference, for example, to balance an amount of detailthe user desires to see against the crowding of data points on thevisualization.

In some embodiments, a data intake and query system 108 automaticallyselects a network address range size to use based characteristics of theidentified network activity data or based on display preferences. Forexample, a user might specify an approximate number of data points todisplay in a visualization, and a data intake and query system 108 canselect, based on the network activity data to be displayed, an addressrange size to use such that the specified number of data points is mostclosely matched.

Although the example address ranges described above correspond to theoctet divisions of an IP address, other points within an IP address canbe used to define network address ranges. In general, any portion of anetwork address can be used to define a range used. For example, anaddress range can be expressed as a prefix of any number of bits of anIP address (e.g., using Classless Inter-Domain Routing (CIDR) notation)or using any other representation of an address range.

In an embodiment, a range of network addresses represented by one axisof a network activity visualization can be the same or different from arange of network addresses represented by the other axis. For example, anetwork activity visualization might include a first axis representingsource network address ranges of the form a.b.*.*, and the samevisualization might include a second axis representing destinationnetwork address ranges of the form a.b.c.*, or vice versa. As describedabove, a size of the address range used for each axis can be selected bydefault, based on user input, or automatically selected based oncharacteristics of the data or specified display preferences.

In an embodiment, each data point can be displayed with a variabledisplay characteristic based on an age or other attribute of theinstances of network activity represented by the data point. Forexample, instances of network activity identified in network activitydata may be associated with one or more timestamps, where each timestampindicates a time at which the instance of network activity started,ended, or otherwise occurred. In an embodiment, the variablecharacteristic might include one or more of: opacity, color, area,shape, and color intensity. For example, data points representinginstances of network activity which occurred further in the pastrelative to a base time (e.g., the current time or a selected point intime) may appear with greater opacity or a lighter color relative todata points representing more current events.

3.3. Interacting with Network Activity Visualizations

In an embodiment, a graphical interface displaying a network activityvisualization as described above further includes interface elementswhich enable users to interact with and explore various aspects of thevisualization. Example interface elements might enable users to zoom inand zoom out on particular regions of a network activity visualization,to view additional information related to selected data points, toselect different metrics upon which to base the display of data points,to view an animated visualization showing network activity over time,and to scan the visualization in the time domain, among other features.

FIG. 20 is a graphical interface including interface elements whichenable users to view additional information related to selected datapoints displayed in a network activity visualization. Similar to FIG. 18, the interface 2002 of FIG. 20 includes a network activityvisualization 2004, where the visualization 2004 includes a first axis2006 and a second axis 2008 corresponding to source network addressranges and destination network address ranges, respectively.

In FIG. 20 , the visualization 2004 includes a plurality of plotted datapoints, including example data points 2010. In an embodiment, thevisualization 2004 further enables users to select data points displayedin the visualization 2004 (e.g., by selecting data points with an inputcursor, hovering an input cursor over data points, etc.), where theselection causes display of an interface element including additionalinformation about instances of network activity represented by the oneor more selected data points.

For example, a data point tooltip 2012 can be generated and displayed inresponse to a user selecting one or more of the data points displayed inthe visualization 2004. The tooltip 2012 includes, for example,information about pairs of network address ranges represented by theselected data point (e.g., “10.141.*.* 10.141.*.*” and“10.141.*.*→10.160.*.*”) and further indicates a number of networkactivity instances associated with each network address range pair(e.g., “212,467 flows” for the network address range pair“10.141.*.*→10.141.*.*” and “115,359 flows” for the network addressrange pair “10.141.*.*→10.160.*.*”). In other embodiments, in responseto selection of one or more data points, an interface 2002 may displayany information about instances of network activity represented by theselected data point, including network address ranges associated withthe data point, one or more metrics associated with the representedinstances of network activity (e.g., a number of represented instancesof network activity, a number of represented network flows, a number ofpackets transferred, a number of bytes transferred, a duration of thenetwork activity, a type of the involved networked devices, etc.), typesof devices associated with the represented instances of networkactivity, a physical location of the devices associated with therepresented instances of network activity, or any other information.

In one embodiment, a user can select a data point to cause display of adifferent interface displaying information about the instances ofnetwork activity represented by the selected data points. For example,in response to selection of a data point, a data intake and query system108 can generate an interface displaying a list of the representedinstances of network activity similar to a search results interface. Asanother example, a data intake and query system 108 might generate aninterface displaying a network topology map including devices associatedwith instances of network activity represented by the selected datapoint, or any other display of the represented instances of networkactivity.

In an embodiment, input can be received to zoom in on a selected portionof a network activity visualization. For example, a user might user aninput device to select a rectangular region 2014 of network activityvisualization 2004 and, in response, the visualization 2004 can beupdated so that the axes 2006 and 2008 correspond to only the selectedregion of the visualization. This may enable a user to obtain a moredetailed view of particular ranges of an address space where avisualization indicates network activity is occurring. In an embodiment,a user can provide other input to zoom out of a visualization that isdisplaying only a subset of an entire network address space.

FIG. 21 is a graphical interface including example interface elementswhich enable various types of interactions and modifications to anetwork activity visualization. In an embodiment, a graphical interface2102 includes network activity visualization 2104 (partially displayed),a time window selector 2106, a data point metric selector 2108,visualization animation controls 2110, and a visualization timeline2112.

In an embodiment, a metric selector 2108 enables the graphical interface2102 to receive input selecting at least one characteristic of theinstances of network activity to be displayed in the visualization 2104,the at least one characteristic including one or more of: a quantity ofinstances of network activity, a number of packets transferred, anamount of data transferred, a duration of time, and a data transferrate. For example, in response to user selection of “sum(bytes)” fromthe metric selector 2108, the visualization 2104 can display data pointswhere the area of each bubble in the chart is displayed with a sizecorresponding to the sum of bytes transferred during the representedinstances of network activity.

In an embodiment, an interface includes elements which enable a networkactivity visualization to be displayed as an animated series of datapoints on a chart illustrating network activity over a period of time.For example, each “frame” of an animated series of data points displayedon a chart can be displayed in a chronological order based on timestampsassociated with instances of network activity represented by the datapoints. Data points displayed in each frame of an animated display candisappear immediately when the animated display is no longer focused onthe point in time corresponding to the data points' timestamps, or thedata points can fade in or fade out around the point in timecorresponding to the timestamps. These animated displays can provide avisual indication of how network activity changes over time, includingchanges in which devices are involved, how much network activity isoccurring, and times of day when activity is heaviest.

In an embodiment, a time range used to generate an animated display canbe the entire time range of the associated animated events (e.g., a timerange bounded by the earliest timestamp and the most recent timestampfound in the displayed instances of network activity), or a time rangeselected by a user (e.g., instances of network activity occurring in thepast hour, instances of network activity occurring from 8:00 AM-11:00 AMthe previous day). For example, a time window selector 2106 may includea list of commonly selected time ranges (e.g., the past hour, theprevious day, the previous week, etc.) for selection by a user. Inresponse to the selection of a time range, a graphical interface 2102can automatically cause an animated display of data points associatedwith the selected time range, or the animated display can start inresponse to additional input received via visualization animationcontrols 2110 (e.g., user input selecting a play button).

In an embodiment, an interface includes interface elements which enablea user to temporally scan an animated display of data points on a chart.As indicated above, an animated display of data points can be displayedbased on a selection of a time range. In an embodiment, visualizationanimation controls 2110 can include buttons used to play, pause, stop,fast forward, and rewind an animated display of data points in thevisualization 2104, so that the user can navigate the animated displayin the time domain.

In an embodiment, an interface includes a timeline interface elementincluding a selectable range of time associated with the displayedinstances of network activity. For example, the graphical interface 2102in FIG. 21 includes a visualization timeline 2112, which includes ahorizontal axis corresponding to time and a vertical axis correspondingto a network activity metric (e.g., the metric selected in data pointmetric selector 2108). The height of the bars shown in the visualizationtimeline 2112, for example, indicate a value for the selected metric ateach point in time (e.g., a number of network flows at each of 9:05 AM,9:10 AM, 9:15 AM, and so forth). In an embodiment, a user can use aninput cursor to select points of time or ranges of time on a timeline2112 and thereby cause display of data points in the visualization 2104corresponding to the selected time point or time range. A user canfurther “scan” the timeline 2112, for example, by sliding an inputcursor horizontally on the timeline 2112 to provide fine-grained controlof a network activity animation.

4.0. EXAMPLE EMBODIMENTS

Examples of some embodiments are represented, without limitation, in thefollowing clauses:

In an embodiment, a method or non-transitory computer readable mediumcomprises: determining, based on data indicating activity of networkeddevices in an information technology environment, instances of networkactivity between the networked devices; causing display of a graphicalrepresentation including a plurality of data points, each data point ofthe plurality of data points representing: a range of source networkaddresses and a range of destination network addresses; and at least oneinstance of network activity, the at least one instance of networkactivity associated with a source network address within the range ofsource network addresses, and a destination network address within therange of destination network addresses.

In an embodiment, a method or non-transitory computer readable mediumcomprises: wherein each data point of the plurality of data points isdisplayed at a location relative to a first axis corresponding to sourcenetwork addresses and a second axis corresponding to destination networkaddresses, and wherein the location corresponds to the range of sourcenetwork addresses and the range of destination network addressesrepresented by the data point.

In an embodiment, a method or non-transitory computer readable mediumcomprises: wherein the range of source network addresses corresponds tonetwork addresses having a same first two bytes of an Internet Protocol(IP) address, and wherein the range of destination network addressescorresponds to network addresses having a same first two bytes of an IPaddress.

In an embodiment, a method or non-transitory computer readable mediumcomprises: wherein the range of source network addresses is the samesize as the range of destination network addresses.

In an embodiment, a method or non-transitory computer readable mediumcomprises: wherein the range of source network addresses is a differentsize from the range of destination network addresses.

In an embodiment, a method or non-transitory computer readable mediumcomprises: wherein at least one of the instances of network activitycorresponds to one or more of: a network session involving a pair ofnetworked computing devices, a network flow involving a pair ofnetworked computing devices, and a network connection involving a pairof networked computing devices.

In an embodiment, a method or non-transitory computer readable mediumcomprises: wherein each data point of the plurality of data points isdisplayed as a shape having an area corresponding to a number ofinstances of network activity represented by the data point.

In an embodiment, a method or non-transitory computer readable mediumcomprises: wherein each data point of the plurality of data points isdisplayed as a shape having an area corresponding to a value associatedwith instances of network activity represented by the data point.

In an embodiment, a method or non-transitory computer readable mediumcomprises: wherein each data point of the plurality of data points isdisplayed as a shape having an area corresponding to at least one valueassociated with instances of network activity represented by the datapoint, wherein the at least one value includes one or more of: a numberof packets transferred, an amount of data transferred, a duration oftime, and a data transfer rate.

In an embodiment, a method or non-transitory computer readable mediumcomprises: receiving a selection of at least one characteristic of theinstances of network activity, the at least one characteristic includingone or more: a quantity of instances of network activity, a number ofpackets transferred, an amount of data transferred, a duration of time,and a data transfer rate; and updating the display of the plurality ofdata points based on the at least one characteristic.

In an embodiment, a method or non-transitory computer readable mediumcomprises: wherein each data point of the plurality of data points isdisplayed at a location relative to a first axis corresponding to sourcenetwork addresses and a second axis corresponding to destination networkaddresses, and wherein a range of the first axis and the second axisincludes an entire network address space.

In an embodiment, a method or non-transitory computer readable mediumcomprises: wherein each data point of the plurality of data points isdisplayed at a location relative to a first axis corresponding to sourcenetwork addresses and a second axis corresponding to destination networkaddresses, wherein a range of the first axis and the second axisincludes an entire network address space, and wherein the entire networkaddress space is one of the Internet Protocol version 4 (IPv4) networkaddress space and the Internet Protocol version 6 (IPv6) network addressspace.

In an embodiment, a method or non-transitory computer readable mediumcomprises: wherein each data point is displayed with a variablecharacteristic relative to an age of instances of network activityassociated with the data point, and wherein the variable characteristicis one or more of: opacity, color, area, shape, and color intensity.

In an embodiment, a method or non-transitory computer readable mediumcomprises: wherein the network activity data includes log data, andwherein at least one of the instances of network activity corresponds toone or more log entries of the log data.

In an embodiment, a method or non-transitory computer readable mediumcomprises: wherein the network activity data includes timestamped eventdata, the timestamped event data generated by a network componentmonitoring network traffic traversing a computer network.

In an embodiment, a method or non-transitory computer readable mediumcomprises: wherein the graphical representation includes a bubble chart,and wherein each data point of the plurality of data points is displayedas a bubble having an area corresponding to at least one characteristicof the instances of network activity represented by the data point.

In an embodiment, a method or non-transitory computer readable mediumcomprises: causing display of an animated series of data points, whereineach instance of the animated series of data points is displayed in achronological order based on timestamps associated with instances ofnetwork activity represented by the data points.

In an embodiment, a method or non-transitory computer readable mediumcomprises: receiving input indicating a range of time associated withthe instances of network activity; and causing display of an animatedseries of data points, wherein the animated series of data points isbased on network events associated with timestamps within the indicatedrange of time, and wherein each instance of the animated series of datapoints is displayed in a chronological order based on timestampsassociated with the instances of network activity.

In an embodiment, a method or non-transitory computer readable mediumcomprises: causing display of an animated series of data points, whereineach instance of the animated series of data points is displayed in achronological order based on timestamps associated with instances ofnetwork activity represented by the data points; receiving input, viathe graphical interface, indicating a request to temporally scan theanimated series of data points, wherein temporally scanning the animatedseries of data points includes one or more of: rewinding,fast-forwarding, pausing, and restarting; and causing display of theanimated series of data points according to the input.

In an embodiment, a method or non-transitory computer readable mediumcomprises: wherein the graphical interface includes a timeline interfaceelement including a selectable range of time associated with theplurality of instances of network activity; receiving, via the timelineinterface element, a selection of a time segment from the range of time;and based on the selected time segment, causing display of data pointsassociated with the selected time segment.

In an embodiment, a method or non-transitory computer readable mediumcomprises: wherein the range of source network addresses and the rangeof destination network addresses are each defined by a portion of anetwork address.

Other examples of these and other embodiments are found throughout thisdisclosure.

5.0. IMPLEMENTATION MECHANISM—HARDWARE OVERVIEW

According to one embodiment, the techniques described herein areimplemented by one or more special-purpose computing devices. Thespecial-purpose computing devices may be desktop computer systems,portable computer systems, handheld devices, networking devices or anyother device that incorporates hard-wired and/or program logic toimplement the techniques. The special-purpose computing devices may behard-wired to perform the techniques, or may include digital electronicdevices such as one or more application-specific integrated circuits(ASICs) or field programmable gate arrays (FPGAs) that are persistentlyprogrammed to perform the techniques, or may include one or more generalpurpose hardware processors programmed to perform the techniquespursuant to program instructions in firmware, memory, other storage, ora combination thereof. Such special-purpose computing devices may alsocombine custom hard-wired logic, ASICs, or FPGAs with custom programmingto accomplish the techniques.

FIG. 22 is a block diagram that illustrates a computer system 2200utilized in implementing the above-described techniques, according to anembodiment. Computer system 2200 may be, for example, a desktopcomputing device, laptop computing device, tablet, smartphone, serverappliance, computing mainframe, multimedia device, handheld device,networking apparatus, or any other suitable device.

Computer system 2200 includes one or more busses 2202 or othercommunication mechanism for communicating information, and one or morehardware processors 2204 coupled with busses 2202 for processinginformation. Hardware processors 2204 may be, for example, generalpurpose microprocessors. Busses 2702 may include various internal and/orexternal components, including, without limitation, internal processoror memory busses, a Serial ATA bus, a PCI Express bus, a UniversalSerial Bus, a HyperTransport bus, an Infiniband bus, and/or any othersuitable wired or wireless communication channel.

Computer system 2200 also includes a main memory 2206, such as a randomaccess memory (RAM) or other dynamic or volatile storage device, coupledto bus 2202 for storing information and instructions to be executed byprocessor 2204. Main memory 2206 also may be used for storing temporaryvariables or other intermediate information during execution ofinstructions to be executed by processor 2204. Such instructions, whenstored in non-transitory storage media accessible to processor 2204,render computer system 2200 a special-purpose machine that is customizedto perform the operations specified in the instructions.

Computer system 2200 further includes one or more read only memories(ROM) 2208 or other static storage devices coupled to bus 2202 forstoring static information and instructions for processor 2204. One ormore storage devices 2210, such as a solid-state drive (SSD), magneticdisk, optical disk, or other suitable non-volatile storage device, isprovided and coupled to bus 2202 for storing information andinstructions.

Computer system 2200 may be coupled via bus 2202 to one or more displays2212 for presenting information to a computer user. For instance,computer system 2200 may be connected via an High-Definition MultimediaInterface (HDMI) cable or other suitable cabling to a Liquid CrystalDisplay (LCD) monitor, and/or via a wireless connection such aspeer-to-peer Wi-Fi Direct connection to a Light-Emitting Diode (LED)television. Other examples of suitable types of displays 2212 mayinclude, without limitation, plasma display devices, projectors, cathoderay tube (CRT) monitors, electronic paper, virtual reality headsets,braille terminal, and/or any other suitable device for outputtinginformation to a computer user. In an embodiment, any suitable type ofoutput device, such as, for instance, an audio speaker or printer, maybe utilized instead of a display 2212.

One or more input devices 2214 are coupled to bus 2202 for communicatinginformation and command selections to processor 2204. One example of aninput device 2214 is a keyboard, including alphanumeric and other keys.Another type of user input device 2214 is cursor control 2216, such as amouse, a trackball, or cursor direction keys for communicating directioninformation and command selections to processor 2204 and for controllingcursor movement on display 2212. This input device typically has twodegrees of freedom in two axes, a first axis (e.g., x) and a second axis(e.g., y), that allows the device to specify positions in a plane. Yetother examples of suitable input devices 2214 include a touch-screenpanel affixed to a display 2212, cameras, microphones, accelerometers,motion detectors, and/or other sensors. In an embodiment, anetwork-based input device 2214 may be utilized. In such an embodiment,user input and/or other information or commands may be relayed viarouters and/or switches on a Local Area Network (LAN) or other suitableshared network, or via a peer-to-peer network, from the input device2214 to a network link 2220 on the computer system 2200.

A computer system 2200 may implement techniques described herein usingcustomized hard-wired logic, one or more ASICs or FPGAs, firmware and/orprogram logic which in combination with the computer system causes orprograms computer system 2200 to be a special-purpose machine. Accordingto one embodiment, the techniques herein are performed by computersystem 2200 in response to processor 2204 executing one or moresequences of one or more instructions contained in main memory 2206.Such instructions may be read into main memory 2206 from another storagemedium, such as storage device 2210. Execution of the sequences ofinstructions contained in main memory 2206 causes processor 2204 toperform the process steps described herein. In alternative embodiments,hard-wired circuitry may be used in place of or in combination withsoftware instructions.

The term “storage media” as used herein refers to any non-transitorymedia that store data and/or instructions that cause a machine tooperate in a specific fashion. Such storage media may comprisenon-volatile media and/or volatile media. Non-volatile media includes,for example, optical or magnetic disks, such as storage device 2210.Volatile media includes dynamic memory, such as main memory 2206. Commonforms of storage media include, for example, a floppy disk, a flexibledisk, hard disk, solid state drive, magnetic tape, or any other magneticdata storage medium, a CD-ROM, any other optical data storage medium,any physical medium with patterns of holes, a RAM, a PROM, an EPROM, aFLASH-EPROM, NVRAM, any other memory chip or cartridge.

Storage media is distinct from but may be used in conjunction withtransmission media. Transmission media participates in transferringinformation between storage media. For example, transmission mediaincludes coaxial cables, copper wire and fiber optics, including thewires that comprise bus 2202. Transmission media can also take the formof acoustic or light waves, such as those generated during radio-waveand infra-red data communications.

Various forms of media may be involved in carrying one or more sequencesof one or more instructions to processor 2204 for execution. Forexample, the instructions may initially be carried on a magnetic disk ora solid state drive of a remote computer. The remote computer can loadthe instructions into its dynamic memory and use a modem to send theinstructions over a network, such as a cable network or cellularnetwork, as modulate signals. A modem local to computer system 2200 canreceive the data on the network and demodulate the signal to decode thetransmitted instructions. Appropriate circuitry can then place the dataon bus 2202. Bus 2202 carries the data to main memory 2206, from whichprocessor 2204 retrieves and executes the instructions. The instructionsreceived by main memory 2206 may optionally be stored on storage device2210 either before or after execution by processor 2204.

A computer system 2200 may also include, in an embodiment, one or morecommunication interfaces 2218 coupled to bus 2202. A communicationinterface 2218 provides a data communication coupling, typicallytwo-way, to a network link 2220 that is connected to a local network2222. For example, a communication interface 2218 may be an integratedservices digital network (ISDN) card, cable modem, satellite modem, or amodem to provide a data communication connection to a corresponding typeof telephone line. As another example, the one or more communicationinterfaces 2218 may include a local area network (LAN) card to provide adata communication connection to a compatible LAN. As yet anotherexample, the one or more communication interfaces 2218 may include awireless network interface controller, such as a 802.11-basedcontroller, Bluetooth controller, Long Term Evolution (LTE) modem,and/or other types of wireless interfaces. In any such implementation,communication interface 2218 sends and receives electrical,electromagnetic, or optical signals that carry digital data streamsrepresenting various types of information.

Network link 2220 typically provides data communication through one ormore networks to other data devices. For example, network link 2220 mayprovide a connection through local network 2222 to a host computer 2224or to data equipment operated by a Service Provider 2226. ServiceProvider 2226, which may for example be an Internet Service Provider(ISP), in turn provides data communication services through a wide areanetwork, such as the world wide packet data communication network nowcommonly referred to as the “Internet” 2228. Local network 2222 andInternet 2228 both use electrical, electromagnetic or optical signalsthat carry digital data streams. The signals through the variousnetworks and the signals on network link 2220 and through communicationinterface 2218, which carry the digital data to and from computer system2200, are example forms of transmission media.

In an embodiment, computer system 2200 can send messages and receivedata, including program code and/or other types of instructions, throughthe network(s), network link 2220, and communication interface 2218. Inthe Internet example, a server 2230 might transmit a requested code foran application program through Internet 2228, ISP 2226, local network2222 and communication interface 2218. The received code may be executedby processor 2204 as it is received, and/or stored in storage device2210, or other non-volatile storage for later execution. As anotherexample, information received via a network link 2220 may be interpretedand/or processed by a software component of the computer system 2200,such as a web browser, application, or server, which in turn issuesinstructions based thereon to a processor 2204, possibly via anoperating system and/or other intermediate layers of softwarecomponents.

In an embodiment, some or all of the systems described herein may be orcomprise server computer systems, including one or more computer systems2200 that collectively implement various components of the system as aset of server-side processes. The server computer systems may includeweb server, application server, database server, and/or otherconventional server components that certain above-described componentsutilize to provide the described functionality. The server computersystems may receive network-based communications comprising input datafrom any of a variety of sources, including without limitationuser-operated client computing devices such as desktop computers,tablets, or smartphones, remote sensing devices, and/or other servercomputer systems.

In an embodiment, certain server components may be implemented in fullor in part using “cloud”-based components that are coupled to thesystems by one or more networks, such as the Internet. The cloud-basedcomponents may expose interfaces by which they provide processing,storage, software, and/or other resources to other components of thesystems. In an embodiment, the cloud-based components may be implementedby third-party entities, on behalf of another entity for whom thecomponents are deployed. In other embodiments, however, the describedsystems may be implemented entirely by computer systems owned andoperated by a single entity.

In an embodiment, an apparatus comprises a processor and is configuredto perform any of the foregoing methods. In an embodiment, anon-transitory computer readable storage medium, storing softwareinstructions, which when executed by one or more processors causeperformance of any of the foregoing methods.

6.0. EXTENSIONS AND ALTERNATIVES

As used herein, the terms “first,” “second,” “certain,” and “particular”are used as naming conventions to distinguish queries, plans,representations, steps, objects, devices, or other items from eachother, so that these items may be referenced after they have beenintroduced. Unless otherwise specified herein, the use of these termsdoes not imply an ordering, timing, or any other characteristic of thereferenced items.

In the foregoing specification, embodiments of the invention have beendescribed with reference to numerous specific details that may vary fromimplementation to implementation. Thus, the sole and exclusive indicatorof what is the invention, and is intended by the applicants to be theinvention, is the set of claims that issue from this application, in thespecific form in which such claims issue, including any subsequentcorrection. In this regard, although specific claim dependencies are setout in the claims of this application, it is to be noted that thefeatures of the dependent claims of this application may be combined asappropriate with the features of other dependent claims and with thefeatures of the independent claims of this application, and not merelyaccording to the specific dependencies recited in the set of claims.Moreover, although separate embodiments are discussed herein, anycombination of embodiments and/or partial embodiments discussed hereinmay be combined to form further embodiments.

Any definitions expressly set forth herein for terms contained in suchclaims shall govern the meaning of such terms as used in the claims.Hence, no limitation, element, property, feature, advantage or attributethat is not expressly recited in a claim should limit the scope of suchclaim in any way. The specification and drawings are, accordingly, to beregarded in an illustrative rather than a restrictive sense.

What is claimed is:
 1. A computer-implemented method, comprising:obtaining, from a data store of a data intake and query system,timestamped event data indicating instances of network activityinvolving a plurality of networked computing devices, wherein each ofthe instances of network activity is identified by: a source device'snetwork address, a destination device's network address, and a timestampassociated with the instance of network activity; and causing display ofan interface including a visualization of the instances of networkactivity, wherein the visualization includes: a first axis and a secondaxis each representing an entire range of network addresses of anInternet Protocol (IP) network address space, and an animated series ofdata points, wherein each data point of the animated series of datapoints is displayed in a chronological order based on the timestampsassociated with the instances of network activity.
 2. Thecomputer-implemented method of claim 1, wherein each data point of theanimated series of data points represents a range of source devicenetwork addresses and a range of destination device network addresses.3. The computer-implemented method of claim 1, wherein each data pointof the animated series of data points represents a range of sourcedevice network addresses and a range of destination device networkaddresses, wherein the range of source device network addresses isdefined by network addresses having a same first two bytes, and whereinthe range of destination device network addresses is defined by networkaddresses having a same first two bytes.
 4. The computer-implementedmethod of claim 1, wherein at least one of the instances of networkactivity corresponds to one or more of: a network session involving apair of networked computing devices, a network flow involving a pair ofnetworked computing devices, and a network connection involving a pairof networked computing devices.
 5. The computer-implemented method ofclaim 1, wherein each data point of the animated series of data pointsis displayed as a shape having an area corresponding to a number ofinstances of network activity represented by the data point.
 6. Thecomputer-implemented method of claim 1, wherein each data point of theanimated series of data points is displayed as a shape having an areacorresponding to a value associated with instances of network activityrepresented by the data point.
 7. The computer-implemented method ofclaim 1, wherein each data point of the animated series of data pointsis displayed as a shape having an area corresponding to at least onevalue associated with instances of network activity represented by thedata point, wherein the at least one value includes at least one of: anumber of packets transferred, an amount of data transferred, a durationof time, or a data transfer rate.
 8. The computer-implemented method ofclaim 1, further comprising: receiving a selection of at least onecharacteristic of the instances of network activity, the at least onecharacteristic including one or more: a quantity of instances of networkactivity, a number of packets transferred, an amount of datatransferred, a duration of time, and a data transfer rate; and updatingthe visualization based on the at least one characteristic.
 9. Thecomputer-implemented method of claim 1, wherein the timestamped eventdata is derived from log data, and wherein at least one of the instancesof network activity corresponds to one or more log entries of the logdata.
 10. The computer-implemented method of claim 1, wherein thevisualization includes a bubble chart, and wherein each data point ofthe animated series of data points is displayed as a bubble having anarea corresponding to at least one characteristic of the instances ofnetwork activity represented by the data point.
 11. Thecomputer-implemented method of claim 1, further comprising: receivinginput indicating a range of time associated with the instances ofnetwork activity; and causing display of the animated series of datapoints based on instances of network activity associated with timestampswithin the indicated range of time.
 12. The computer-implemented methodof claim 1, further comprising: receiving input requesting to temporallyscan the animated series of data points, wherein temporally scanning theanimated series of data points includes one or more of: rewinding,fast-forwarding, pausing, and restarting; and causing display of theanimated series of data points according to the input.
 13. Anon-transitory computer-readable storage medium storing instructionswhich, when executed by one or more processors, cause performance ofoperations comprising: obtaining, from a data store of a data intake andquery system, timestamped event data indicating instances of networkactivity involving a plurality of networked computing devices, whereineach of the instances of network activity is identified by: a sourcedevice's network address, a destination device's network address, and atimestamp associated with the instance of network activity; and causingdisplay of an interface including a visualization of the instances ofnetwork activity, wherein the visualization includes: a first axis and asecond axis each representing an entire range of network addresses of anInternet Protocol (IP) network address space, and an animated series ofdata points, wherein each data point of the animated series of datapoints is displayed in a chronological order based on the timestampsassociated with the instances of network activity.
 14. Thenon-transitory computer-readable storage medium of claim 13, whereineach data point of the animated series of data points represents a rangeof source device network addresses and a range of destination devicenetwork addresses.
 15. The non-transitory computer-readable storagemedium of claim 13, wherein the instructions, when executed by one ormore processors, further cause performance of operations comprising:receiving input indicating a range of time associated with the instancesof network activity; and causing display of the animated series of datapoints based on instances of network activity associated with timestampswithin the indicated range of time.
 16. The non-transitorycomputer-readable storage medium of claim 13, wherein the instructions,when executed by one or more processors, further cause performance ofoperations comprising: receiving input requesting to temporally scan theanimated series of data points, wherein temporally scanning the animatedseries of data points includes one or more of: rewinding,fast-forwarding, pausing, and restarting; and causing display of theanimated series of data points according to the input.
 17. An apparatus,comprising: one or more processors; and a non-transitorycomputer-readable storage medium storing instructions which, whenexecuted by the one or more processors, cause the apparatus to: obtain,from a data store of a data intake and query system, timestamped eventdata indicating instances of network activity involving a plurality ofnetworked computing devices, wherein each of the instances of networkactivity is identified by: a source device's network address, adestination device's network address, and a timestamp associated withthe instance of network activity; and cause display of an interfaceincluding a visualization of the instances of network activity, whereinthe visualization includes: a first axis and a second axis eachrepresenting an entire range of network addresses of an InternetProtocol (IP) network address space, and an animated series of datapoints, wherein each data point of the animated series of data points isdisplayed in a chronological order based on the timestamps associatedwith the instances of network activity.
 18. The apparatus of claim 17,wherein each data point of the animated series of data points representsa range of source device network addresses and a range of destinationdevice network addresses.
 19. The apparatus of claim 17, wherein theinstructions, when executed by the one or more processors, further causethe apparatus to: receive input indicating a range of time associatedwith the instances of network activity; and cause display of theanimated series of data points based on instances of network activityassociated with timestamps within the indicated range of time.
 20. Theapparatus of claim 17, wherein the instructions, when executed by theone or more processors, further cause the apparatus to: receive inputrequesting to temporally scan the animated series of data points,wherein temporally scanning the animated series of data points includesone or more of: rewinding, fast-forwarding, pausing, and restarting; andcause display of the animated series of data points according to theinput.